{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T16:39:13Z","timestamp":1782491953717,"version":"3.54.5"},"reference-count":510,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.bspc.2025.109285","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T19:14:01Z","timestamp":1766171641000},"page":"109285","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Machine Learning-assisted Quorum Sensing Monitoring and Control Systems for Precision Gene Regulation: Revolutionizing Synthetic Biology and Autonomous Therapeutic Applications"],"prefix":"10.1016","volume":"115","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3320-4072","authenticated-orcid":false,"given":"Dang Anh","family":"Tuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pham Vu","family":"Nhat Uyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2025.109285_b0005","series-title":"Microbial Biotechnology: Role in Ecological Sustainability and Research","first-page":"75","article-title":"Quorum sensing and environmental sustainability","author":"Banerjee","year":"2022"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b0010","doi-asserted-by":"crossref","first-page":"e00745","DOI":"10.1128\/mbio.00745-22","article-title":"Bacterial quorum sensing allows graded and bimodal cellular responses to variations in population density","volume":"13","author":"Rattray","year":"2022","journal-title":"MBio"},{"key":"10.1016\/j.bspc.2025.109285_b0015","doi-asserted-by":"crossref","unstructured":"M. Konda, R. Tippani, M. Porika, and L. Banoth, \u201cQuorum Sensing: A New Target for Anti-infective Drug Therapy,\u201d. In: Quorum Quenching: A Chemical Biological Approach for Microbial Biofilm Mitigation and Drug Development, vol. 22, N. R. Maddela, V. Reddy Kondakindi, and R. Pabbati Eds.: Royal Society of Chemistry, 2023, p. 0.","DOI":"10.1039\/BK9781837671380-00250"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0020","doi-asserted-by":"crossref","first-page":"144","DOI":"10.3390\/synbio1020010","article-title":"An engineered escherichia coli community for studying quorum sensing","volume":"1","author":"Li","year":"2023","journal-title":"SynBio"},{"issue":"06","key":"10.1016\/j.bspc.2025.109285_b0025","first-page":"1188","article-title":"An overview of biofilm as a virulence factor for bacteria to survive in the harsh environment","volume":"3","author":"Al-Tayawi","year":"2023","journal-title":"Int. J. Medical Sci. Clinical Res. Studies"},{"key":"10.1016\/j.bspc.2025.109285_b0030","doi-asserted-by":"crossref","unstructured":"O. O. Bello et al., \u201cOccurrence and Role of Bacterial Biofilms in Different Systems,\u201d 2023.","DOI":"10.59393\/amb23390304"},{"key":"10.1016\/j.bspc.2025.109285_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2024.172668","article-title":"Protective effects of antibiotic resistant bacteria on susceptibles in biofilm: influential factors, mechanism, and modeling","volume":"930","author":"Xu","year":"2024","journal-title":"Sci. Total Environ."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0040","doi-asserted-by":"crossref","first-page":"83","DOI":"10.4014\/kjmb.1205.05011","article-title":"Bacterial quorum sensing and quorum quenching for the inhibition of biofilm formation","volume":"40","author":"Lee","year":"2012","journal-title":"Microbiol. Biotechnol. Lett."},{"key":"10.1016\/j.bspc.2025.109285_b0045","doi-asserted-by":"crossref","first-page":"39","DOI":"10.4028\/www.scientific.net\/AMM.295-298.39","article-title":"Conceivable bioremediation techniques based on quorum sensing","volume":"295","author":"Liao","year":"2013","journal-title":"Appl. Mech. Mater."},{"key":"10.1016\/j.bspc.2025.109285_b0050","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s10482-013-0082-3","article-title":"Bacterial quorum sensing: circuits and applications","volume":"105","author":"Garg","year":"2014","journal-title":"Antonie Van Leeuwenhoek"},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b0055","doi-asserted-by":"crossref","first-page":"5043","DOI":"10.1021\/acs.nanolett.7b02270","article-title":"Quorum-quenching human designer cells for closed-loop control of Pseudomonas aeruginosa biofilms","volume":"17","author":"Sedlmayer","year":"2017","journal-title":"Nano Lett."},{"key":"10.1016\/j.bspc.2025.109285_b0060","series-title":"Proceedings of the 30th Chinese Control Conference","first-page":"6605","article-title":"Synchronized switching induced by colored noise in the genetic toggle switch systems coupled by quorum sensing mechanism","author":"Wang","year":"2011"},{"key":"10.1016\/j.bspc.2025.109285_b0065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40643-014-0024-6","article-title":"Programming the group behaviors of bacterial communities with synthetic cellular communication","volume":"1","author":"Kong","year":"2014","journal-title":"Bioresources and Bioprocessing"},{"key":"10.1016\/j.bspc.2025.109285_b0070","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1007\/s11802-019-4073-5","article-title":"The mechanisms and applications of quorum sensing (QS) and quorum quenching (QQ)","volume":"18","author":"Zhang","year":"2019","journal-title":"J. Ocean Univ. China"},{"key":"10.1016\/j.bspc.2025.109285_b0075","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2022.869509","article-title":"Exploiting information and control theory for directing gene expression in cell populations","volume":"13","author":"Henrion","year":"2022","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.bspc.2025.109285_b0080","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.97754","article-title":"Light-driven synchronization of optogenetic clocks","volume":"13","author":"Cannarsa","year":"2024","journal-title":"Elife"},{"key":"10.1016\/j.bspc.2025.109285_b0085","doi-asserted-by":"crossref","unstructured":"L. E. B\u00e4cker, K. Broux, L. Weckx, S. Khanal, and A. Aertsen, \u201cTuning and functionalization of logic gates for time resolved population programming,\u201d 2024.","DOI":"10.1101\/2024.05.14.593743"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0090","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1186\/s13068-023-02416-x","article-title":"Construction of cascade circuits for dynamic temporal regulation and its application to PHB production","volume":"16","author":"Li","year":"2023","journal-title":"Biotechnology for Biofuels and Bioproducts"},{"key":"10.1016\/j.bspc.2025.109285_b0095","doi-asserted-by":"crossref","DOI":"10.1093\/jimb\/kuab088","article-title":"Practical genetic control strategies for industrial bioprocesses","volume":"49","author":"Moore","year":"2022","journal-title":"J. Ind. Microbiol. Biotechnol."},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b0100","doi-asserted-by":"crossref","first-page":"14274","DOI":"10.1021\/acsnano.2c04405","article-title":"Programming dissipation systems by DNA timer for temporally regulating enzyme catalysis and nanostructure assembly","volume":"16","author":"Qin","year":"2022","journal-title":"ACS Nano"},{"key":"10.1016\/j.bspc.2025.109285_b0105","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2021.704681","article-title":"A timed off-switch for dynamic control of gene expression in Corynebacterium glutamicum","volume":"9","author":"Siebert","year":"2021","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"11","key":"10.1016\/j.bspc.2025.109285_b0110","doi-asserted-by":"crossref","first-page":"6587","DOI":"10.1093\/nar\/gkac476","article-title":"A pathway independent multi-modular ordered control system based on thermosensors and CRISPRi improves bioproduction in Bacillus subtilis","volume":"50","author":"Yu","year":"2022","journal-title":"Nucleic Acids Res."},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b0115","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1021\/acssynbio.1c00073","article-title":"Multilayer genetic circuits for dynamic regulation of metabolic pathways","volume":"10","author":"Cui","year":"2021","journal-title":"ACS Synth. Biol."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b0120","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1021\/acssynbio.1c00008","article-title":"Design of synthetic quorum sensing achieving induction timing-independent signal stabilization for dynamic metabolic engineering of E. coli","volume":"10","author":"Soma","year":"2021","journal-title":"ACS Synth. Biol."},{"issue":"13","key":"10.1016\/j.bspc.2025.109285_b0125","doi-asserted-by":"crossref","first-page":"5062","DOI":"10.1021\/acs.jafc.3c00176","article-title":"Application of quorum sensing in metabolic engineering","volume":"71","author":"Cao","year":"2023","journal-title":"J. Agric. Food Chem."},{"key":"10.1016\/j.bspc.2025.109285_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemosphere.2024.142983","article-title":"Regulation and application of quorum sensing on anaerobic digestion system","author":"He","year":"2024","journal-title":"Chemosphere"},{"issue":"51","key":"10.1016\/j.bspc.2025.109285_b0135","doi-asserted-by":"crossref","first-page":"25562","DOI":"10.1073\/pnas.1911144116","article-title":"Development of an autonomous and bifunctional quorum-sensing circuit for metabolic flux control in engineered Escherichia coli","volume":"116","author":"Dinh","year":"2019","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.bspc.2025.109285_b0140","series-title":"Current Developments in Biotechnology and Bioengineering","first-page":"115","article-title":"Synthetic regulatory tools to engineer microbial cell factories for chemical production","author":"Jang","year":"2019"},{"key":"10.1016\/j.bspc.2025.109285_b0145","unstructured":"H. S. Monges, \u201cProducing high-value chemicals in Escherichia coli through synthetic biology and metabolic Engineering,\u201d 2019."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0150","doi-asserted-by":"crossref","first-page":"2182","DOI":"10.1038\/s41467-022-29933-x","article-title":"Redesigning regulatory components of quorum-sensing system for diverse metabolic control","volume":"13","author":"Ge","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b0155","doi-asserted-by":"crossref","DOI":"10.1128\/mSystems.00414-20","article-title":"Rich repertoire of quorum sensing protein coding sequences in CPR and DPANN associated with interspecies and interkingdom communication","volume":"5","author":"Bernard","year":"2020","journal-title":"Msystems"},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b0160","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1111\/2041-210X.13894","article-title":"Synthetic microbial consortia with programmable ecological interactions","volume":"13","author":"Li","year":"2022","journal-title":"Methods Ecol. Evol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0165","doi-asserted-by":"crossref","first-page":"114","DOI":"10.3390\/metabo13010114","article-title":"The role of quorum sensing molecules in bacterial\u2013plant interactions","volume":"13","author":"Majdura","year":"2023","journal-title":"Metabolites"},{"key":"10.1016\/j.bspc.2025.109285_b0170","doi-asserted-by":"crossref","unstructured":"S. Wu et al., \u201cDeciphering and Constructing the Quorum Sensing Language \u201cInterpreter\u201d Ecosystem for Microbial Community,\u201d 2024.","DOI":"10.21203\/rs.3.rs-3975227\/v1"},{"key":"10.1016\/j.bspc.2025.109285_b0175","doi-asserted-by":"crossref","first-page":"705","DOI":"10.3389\/fbioe.2020.00705","article-title":"Toward engineering biosystems with emergent collective functions","volume":"8","author":"Gorochowski","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"10.1016\/j.bspc.2025.109285_b0180","doi-asserted-by":"crossref","first-page":"834","DOI":"10.3389\/fbioe.2020.00834","article-title":"From microbial communities to distributed computing systems","volume":"8","author":"Karkaria","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0185","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1038\/s41467-018-05046-2","article-title":"Tools for engineering coordinated system behaviour in synthetic microbial consortia","volume":"9","author":"Kylilis","year":"2018","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0190","doi-asserted-by":"crossref","first-page":"22","DOI":"10.3390\/life9010022","article-title":"Bottom-up approaches to synthetic cooperation in microbial communities","volume":"9","author":"Rodr\u00edguez Amor","year":"2019","journal-title":"Life"},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b0195","first-page":"2517","article-title":"\u201cEngineering microbial consortia through synthetic biology approach,\u201d Sheng wu Gong Cheng xue bao=","volume":"39","author":"Zhang","year":"2023","journal-title":"Chin. J. Biotechnol."},{"key":"10.1016\/j.bspc.2025.109285_b0200","doi-asserted-by":"crossref","DOI":"10.47852\/bonviewMEDIN42024120","article-title":"Machine learning in genomics: applications in whole genome sequencing, whole exome sequencing, single-cell genomics, and spatial transcriptomics","author":"Adeyanju","year":"2024","journal-title":"Medinformatics"},{"key":"10.1016\/j.bspc.2025.109285_b0205","series-title":"How Machine Learning Is Innovating Today's World","first-page":"201","article-title":"ML applications in Healthcare","author":"Shaik","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b0210","first-page":"1","article-title":"Advanced metaheuristic optimization techniques in applications of deep neural networks: a review","author":"Abd Elaziz","year":"2021","journal-title":"Neural Comput. & Applic."},{"key":"10.1016\/j.bspc.2025.109285_b0215","series-title":"2021 40th Chinese Control Conference (CCC)","first-page":"7100","article-title":"A new feature selection algorithm based on deep q-network","author":"Li","year":"2021"},{"key":"10.1016\/j.bspc.2025.109285_b0220","series-title":"Handbook of Deep Learning in Biomedical Engineering","first-page":"61","article-title":"Application, algorithm, tools directly related to deep learning","author":"Nisha","year":"2021"},{"key":"10.1016\/j.bspc.2025.109285_b0225","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csbj.2021.06.030","article-title":"Integration strategies of multi-omics data for machine learning analysis","volume":"19","author":"Picard","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0230","first-page":"26","article-title":"Integrative machine learning approaches for multi-omics data analysis in cancer research","volume":"1","author":"Shoaib","year":"2024","journal-title":"Int. J. Health and Medical"},{"key":"10.1016\/j.bspc.2025.109285_b0235","series-title":"Computer Aided Chemical Engineering","first-page":"919","article-title":"Reinforcement learning for batch-to-batch bioprocess optimisation","author":"Petsagkourakis","year":"2019"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b0240","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1042\/BST20200008","article-title":"Reinforcement learning in synthetic gene circuits","volume":"48","author":"Racovita","year":"2020","journal-title":"Biochem. Soc. Trans."},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b0245","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007783","article-title":"Deep reinforcement learning for the control of microbial co-cultures in bioreactors","volume":"16","author":"Treloar","year":"2020","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b0250","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.coisb.2020.10.006","article-title":"Self-adaptive biosystems through tunable genetic parts and circuits","volume":"24","author":"Bartoli","year":"2020","journal-title":"Curr. Opin. Syst. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b0255","doi-asserted-by":"crossref","unstructured":"P. Bhattacharya, K. Raman, and A. K. Tangirala, \u201cA generic systems-theoretic approach to identify biological networks capable of adaptation,\u201d bioRxiv, p. 2021.05. 27.445914, 2021.","DOI":"10.1101\/2021.05.27.445914"},{"key":"10.1016\/j.bspc.2025.109285_b0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.mbs.2023.108984","article-title":"On biological networks capable of robust adaptation in the presence of uncertainties: a linear systems-theoretic approach","volume":"358","author":"Bhattacharya","year":"2023","journal-title":"Math. Biosci."},{"key":"10.1016\/j.bspc.2025.109285_b0265","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/B978-0-323-90571-8.00015-8","article-title":"Understanding the molecular and genetics determinants of microbial adaptation in changing environment","author":"Dasauni","year":"2022","journal-title":"Microbiome under Changing Climate: Elsevier"},{"key":"10.1016\/j.bspc.2025.109285_b0270","doi-asserted-by":"crossref","unstructured":"S. Prakash et al., \u201cEngineered sensor bacteria evolve master-level gameplay through accelerated adaptation,\u201d bioRxiv, p. 2022.04. 22.489191, 2022.","DOI":"10.1101\/2022.04.22.489191"},{"key":"10.1016\/j.bspc.2025.109285_b0275","doi-asserted-by":"crossref","unstructured":"J. B. Rattray, P. Kramer, J. Gurney, S. Thomas, and S. P. Brown, \u201cThe dynamic response of quorum-sensing to density is robust to signal supplementation and signal synthase knockouts,\u201d bioRxiv, p. 2022.09. 12.507654, 2022.","DOI":"10.1101\/2022.09.12.507654"},{"key":"10.1016\/j.bspc.2025.109285_b0280","doi-asserted-by":"crossref","unstructured":"R. Preiser, \u201cComplex adaptive systems,\u201d. In: Elgar Encyclopedia of Interdisciplinarity and Transdisciplinarity, F. Darbellay Ed., 2024, ch. 19, pp. 86\u201390.","DOI":"10.4337\/9781035317967.ch19"},{"key":"10.1016\/j.bspc.2025.109285_b0285","doi-asserted-by":"crossref","unstructured":"W. Ng, \u201cEvaluating the potential of applying machine learning tools to metabolic pathway optimization,\u201d 2020.","DOI":"10.20944\/preprints202008.0543.v1"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0290","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s43393-022-00115-6","article-title":"Advances and applications of machine learning and intelligent optimization algorithms in genome-scale metabolic network models","volume":"3","author":"Bai","year":"2023","journal-title":"Syst. Microbiol. Biomanuf."},{"key":"10.1016\/j.bspc.2025.109285_b0295","series-title":"Computer Aided Chemical Engineering","first-page":"2601","article-title":"Machine learning-supported cybergenetic modeling, optimization and control for synthetic microbial communities","author":"Espinel-R\u00edos","year":"2023"},{"key":"10.1016\/j.bspc.2025.109285_b0300","first-page":"1","article-title":"Deep learning approaches for predictive modeling and optimization of metabolic fluxes in engineered microorganism","author":"Srikanth","year":"2023","journal-title":"Int. J. Res. Sci. Amp"},{"key":"10.1016\/j.bspc.2025.109285_b0305","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ymben.2024.02.013","article-title":"Machine learning predicts system-wide metabolic flux control in cyanobacteria","volume":"82","author":"Kugler","year":"2024","journal-title":"Metab. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0310","doi-asserted-by":"crossref","first-page":"13","DOI":"10.4018\/IJAIML.2019010102","article-title":"Quorum sensing digital simulations for the emergence of scalable and cooperative artificial networks","volume":"9","author":"Djezzar","year":"2019","journal-title":"Int. J. Artificial Intelligence and Machine Learning (IJAIML)"},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b0315","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1021\/acssynbio.0c00129","article-title":"Biosystems design by machine learning","volume":"9","author":"Volk","year":"2020","journal-title":"ACS Synth. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b0320","article-title":"Cell factory design with advanced metabolic modelling empowered by artificial intelligence","author":"Lu","year":"2024","journal-title":"Metab. Eng."},{"key":"10.1016\/j.bspc.2025.109285_b0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.cej.2024.153148","article-title":"Design and analysis of quorum sensing language \u201cInterpreter\u201d ecosystem for microbial community","volume":"496","author":"Wu","year":"2024","journal-title":"Chem. Eng. J."},{"issue":"12","key":"10.1016\/j.bspc.2025.109285_b0330","doi-asserted-by":"crossref","first-page":"21328","DOI":"10.1109\/JIOT.2025.3546874","article-title":"Toward visual interaction: hand segmentation by combining 3-D graph deep learning and laser point cloud for intelligent rehabilitation","volume":"12","author":"Xing","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.bspc.2025.109285_b0335","doi-asserted-by":"crossref","unstructured":"Z. Xing et al., \u201cIntelligent rehabilitation in an aging population: empowering human-machine interaction for hand function rehabilitation through 3D deep learning and point cloud,\u201d (in English), Frontiers in Computational Neuroscience, Original Research vol. Volume 19 - 2025, 2025-May-02 2025, doi: 10.3389\/fncom.2025.1543643.","DOI":"10.3389\/fncom.2025.1543643"},{"key":"10.1016\/j.bspc.2025.109285_b0340","series-title":"Machine Vision in Plant Leaf Disease Detection for Sustainable Agriculture","first-page":"131","article-title":"Deploying CNN-ResNet50-BiLSTM for paddy leaf disease detection","author":"Basitur Rahman Bappi","year":"2025"},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b0345","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202400566","article-title":"A novel mixed convolution transformer model for the fast and accurate diagnosis of glioma subtypes","volume":"7","author":"Nobel","year":"2025","journal-title":"Adv. Intell. Syst."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b0350","doi-asserted-by":"crossref","first-page":"4452","DOI":"10.1109\/JBHI.2025.3543028","article-title":"CRT: a convolutional recurrent transformer for automatic sleep state detection","volume":"29","author":"Nobel","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0355","doi-asserted-by":"crossref","first-page":"534","DOI":"10.24815\/jpsi.v13i2.44826","article-title":"Implementation of PJBL-STEM learning to improve students' higher order thinking skills in direct current electricity","volume":"13","author":"Salmah","year":"2025","journal-title":"Jurnal Pendidikan Sains Indonesia (indonesian Journal of Science Education)"},{"key":"10.1016\/j.bspc.2025.109285_b0360","article-title":"Accelerated and accurate cervical cancer diagnosis using a novel stacking ensemble method with explainable AI","volume":"56","author":"Siddiqui","year":"2025","journal-title":"Inf. Med. Unlocked"},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b0365","doi-asserted-by":"crossref","first-page":"6583","DOI":"10.1007\/s00521-025-10973-5","article-title":"ViT-SENet-Tom: machine learning-based novel hybrid squeeze\u2013excitation network and vision transformer framework for tomato fruits classification","volume":"37","author":"Swapno","year":"2025","journal-title":"Neural Comput. & Applic."},{"key":"10.1016\/j.bspc.2025.109285_b0370","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.104168","article-title":"Accelerated and precise skin cancer detection through an enhanced machine learning pipeline for improved diagnostic accuracy","volume":"25","author":"Swapno","year":"2025","journal-title":"Results Eng."},{"key":"10.1016\/j.bspc.2025.109285_b0375","doi-asserted-by":"crossref","unstructured":"E. Acevedo, A. Acevedo, F. Felipe, and P. Avil\u00e9s, \u201cArtificial intelligence tools for pattern recognition,\u201d. In: Second International Workshop on Pattern Recognition, 2017, vol. 10443: SPIE, p. 1044302.","DOI":"10.1117\/12.2280310"},{"key":"10.1016\/j.bspc.2025.109285_b0380","doi-asserted-by":"crossref","unstructured":"J. A. Parray, S. Jan, M. Y. Mir, N. Shameem, and A. N. Kamili, \u201cQuorum Sensing: Melody Beneath the Ground,\u201d Plant Microbiome: Stress Response, pp. 201-215, 2018.","DOI":"10.1007\/978-981-10-5514-0_9"},{"key":"10.1016\/j.bspc.2025.109285_b0385","doi-asserted-by":"crossref","unstructured":"X. Li, T. Yang, Y. Shi, and F. Lu, \u201cApplication Methods of Artificial Neural Network in Pattern Recognition,\u201d. In: 2020 13th International Conference on Intelligent Computation Technology and Automation (ICICTA), 2020: IEEE, pp. 306-309.","DOI":"10.1109\/ICICTA51737.2020.00071"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0390","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1146\/annurev-chembioeng-101519-124728","article-title":"Quorum sensing communication: molecularly connecting cells, their neighbors, and even devices","volume":"11","author":"Wang","year":"2020","journal-title":"Annu. Rev. Chem. Biomol. Eng."},{"key":"10.1016\/j.bspc.2025.109285_b0395","series-title":"Machine Learning and Pattern Recognition Methods in Chemistry from Multivariate and Data Driven Modeling","first-page":"47","article-title":"Multivariate pattern recognition by machine learning methods","author":"Razzaghi","year":"2023"},{"key":"10.1016\/j.bspc.2025.109285_b0400","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1007\/s00018-019-03326-8","article-title":"Quorum sensing for population-level control of bacteria and potential therapeutic applications","volume":"77","author":"Wu","year":"2020","journal-title":"Cell. Mol. Life Sci."},{"key":"10.1016\/j.bspc.2025.109285_b0405","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2024.121697","article-title":"Enhanced AHL-mediated quorum sensing accelerates the start-up of biofilm reactors by elevating the fitness of fast-growing bacteria in sludge and biofilm communities","volume":"257","author":"Xiong","year":"2024","journal-title":"Water Res."},{"key":"10.1016\/j.bspc.2025.109285_b0410","series-title":"2021 IEEE Statistical Signal Processing Workshop (SSP)","first-page":"406","article-title":"Unknown signal detection in interference and noise using hidden Markov models","author":"Ford","year":"2021"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0415","doi-asserted-by":"crossref","first-page":"3139","DOI":"10.1038\/s41467-021-23336-0","article-title":"Synthetic neural-like computing in microbial consortia for pattern recognition","volume":"12","author":"Li","year":"2021","journal-title":"Nat. Commun."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b0420","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007166","article-title":"A neural network model predicts community-level signaling states in a diverse microbial community","volume":"15","author":"Silva","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b0425","article-title":"Effective data filtering is prerequisite for robust microbial association network construction","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.bspc.2025.109285_b0430","unstructured":"C. M. H. Ankita D. Jain, \u201cNoise reduction using machine learning,\u201d 2019."},{"key":"10.1016\/j.bspc.2025.109285_b0435","article-title":"Real time monitoring of microbial enzymatic pathways","author":"Contag","year":"2008","journal-title":"Ed: Google Patents"},{"key":"10.1016\/j.bspc.2025.109285_b0440","article-title":"Machine learning of stochastic gene network phenotypes","author":"Park","year":"2019","journal-title":"BioRxiv"},{"key":"10.1016\/j.bspc.2025.109285_b0445","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3163-0","article-title":"Predictive modelling using pathway scores: robustness and significance of pathway collections","volume":"20","author":"Segura-Lepe","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.bspc.2025.109285_b0450","series-title":"2020 16th international conference on distributed computing in sensor systems (DCOSS)","first-page":"213","article-title":"Predictive and explainable machine learning for industrial internet of things applications","author":"Christou","year":"2020"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0455","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41540-021-00199-1","article-title":"Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures","volume":"7","author":"Golriz Khatami","year":"2021","journal-title":"npj Syst. Biol. Appl."},{"issue":"11","key":"10.1016\/j.bspc.2025.109285_b0460","doi-asserted-by":"crossref","first-page":"3145","DOI":"10.1021\/acssynbio.0c00394","article-title":"An automated model test system for systematic development and improvement of gene expression models","volume":"9","author":"Reis","year":"2020","journal-title":"ACS Synth. Biol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0465","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1186\/s12859-022-04787-8","article-title":"Improved prediction of gene expression through integrating cell signalling models with machine learning","volume":"23","author":"Al Taweraqi","year":"2022","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.bspc.2025.109285_b0470","doi-asserted-by":"crossref","unstructured":"D. Gerngross, N. Beerenwinkel, and S. Panke, \u201cSystematic investigation of synthetic operon designs enables prediction and control of expression levels of multiple proteins,\u201d bioRxiv, p. 2022.06. 10.495604, 2022.","DOI":"10.1101\/2022.06.10.495604"},{"issue":"10","key":"10.1016\/j.bspc.2025.109285_b0475","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.3390\/electronics13101799","article-title":"Data-driven prediction model for analysis of sensor data","volume":"13","author":"Yotov","year":"2024","journal-title":"Electronics"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0480","doi-asserted-by":"crossref","DOI":"10.1098\/rsos.211475","article-title":"Machine learning methods trained on simple models can predict critical transitions in complex natural systems","volume":"9","author":"Deb","year":"2022","journal-title":"R. Soc. Open Sci."},{"key":"10.1016\/j.bspc.2025.109285_b0485","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2022.851450","article-title":"Interfacing machine learning and microbial omics: a promising means to address environmental challenges","volume":"13","author":"McElhinney","year":"2022","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.bspc.2025.109285_b0490","series-title":"In Proceedings of the IEEE\/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems","first-page":"9","article-title":"A hybrid approach combining control theory and AI for engineering self-adaptive systems","author":"Caldas","year":"2020"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b0495","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/TBCAS.2015.2458435","article-title":"Designing genetic feedback controllers","volume":"9","author":"Harris","year":"2015","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"10.1016\/j.bspc.2025.109285_b0500","doi-asserted-by":"crossref","unstructured":"M. Zile, \u201cIntelligent and Adaptive Control,\u201d. In: Microgrid Architectures, Control and Protection Methods: Springer, Cham, 2020, pp. 423-446.","DOI":"10.1007\/978-3-030-23723-3_17"},{"key":"10.1016\/j.bspc.2025.109285_b0505","doi-asserted-by":"crossref","first-page":"51","DOI":"10.54254\/2755-2721\/39\/20230577","article-title":"Design and analysis of adaptive control systems: adaptive control algorithms using machine learning","volume":"39","author":"Zhou","year":"2024","journal-title":"Appl. Computational Eng."},{"key":"10.1016\/j.bspc.2025.109285_b0510","series-title":"2018 European Control Conference (ECC)","first-page":"465","article-title":"Real-time optimization of uncertain process systems via modifier adaptation and gaussian processes","author":"de Avila Ferreira","year":"2018"},{"key":"10.1016\/j.bspc.2025.109285_b0515","doi-asserted-by":"crossref","unstructured":"M. S. Morais and A. Araujo, \u201cReal-time optimization strategy of the alcoholic fermentation process using metamodeling,\u201d 2023.","DOI":"10.21203\/rs.3.rs-2636360\/v1"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0520","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1038\/s41598-023-27998-2","article-title":"Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing","volume":"13","author":"Williams","year":"2023","journal-title":"Sci. Rep."},{"issue":"14","key":"10.1016\/j.bspc.2025.109285_b0525","doi-asserted-by":"crossref","first-page":"8188","DOI":"10.1093\/nar\/gkaa602","article-title":"Developing an endogenous quorum-sensing based CRISPRi circuit for autonomous and tunable dynamic regulation of multiple targets in Streptomyces","volume":"48","author":"Tian","year":"2020","journal-title":"Nucleic Acids Res."},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b0530","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1042\/BST20221542","article-title":"Applications of artificial intelligence and machine learning in dynamic pathway engineering","volume":"51","author":"Merzbacher","year":"2023","journal-title":"Biochem. Soc. Trans."},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b0535","doi-asserted-by":"crossref","first-page":"4908","DOI":"10.1002\/rnc.6021","article-title":"Self\u2010regulation in a stochastic model of chemical self\u2010replication","volume":"33","author":"Borri","year":"2023","journal-title":"Int. J. Robust Nonlinear Control"},{"issue":"10","key":"10.1016\/j.bspc.2025.109285_b0540","doi-asserted-by":"crossref","first-page":"8992","DOI":"10.1021\/acsnano.2c09908","article-title":"Self-regulated and bidirectional communication in synthetic cell communities","volume":"17","author":"Ji","year":"2023","journal-title":"ACS Nano"},{"key":"10.1016\/j.bspc.2025.109285_b0545","doi-asserted-by":"crossref","DOI":"10.1016\/j.micres.2024.127720","article-title":"Self-regulated efficient production of L-threonine via an artificial quorum sensing system in engineered Escherichia coli","volume":"284","author":"Song","year":"2024","journal-title":"Microbiol. Res."},{"key":"10.1016\/j.bspc.2025.109285_b0550","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.ymben.2024.02.001","article-title":"A self-regulated network for dynamically balancing multiple precursors in complex biosynthetic pathways","volume":"82","author":"Zou","year":"2024","journal-title":"Metab. Eng."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0555","first-page":"23","article-title":"Quorum sensing: a molecular cell communication in bacterial cells","volume":"5","author":"Kumar","year":"2018","journal-title":"J. Biomedical Sci."},{"key":"10.1016\/j.bspc.2025.109285_b0560","first-page":"89","article-title":"LuxR solos are becoming major players in cell\u2013cell communication in bacteria","volume":"5","author":"Venturi","year":"2015","journal-title":"Ed: Frontiers Media SA"},{"key":"10.1016\/j.bspc.2025.109285_b0565","doi-asserted-by":"crossref","unstructured":"M. H. B. Siam, A. S. Sirajee, M. B. H. Limon, M. A. Hossain, and M. Sultana, \u201cIn silico identification of quorum sensing inhibitors against LasR protein in a clinical isolate of multidrug resistant Pseudomonas aeruginosa DMC-27b,\u201d F1000Research. vol. 13, p. 62, 2024.","DOI":"10.12688\/f1000research.131728.1"},{"issue":"10","key":"10.1016\/j.bspc.2025.109285_b0570","doi-asserted-by":"crossref","first-page":"e00263","DOI":"10.1128\/iai.00263-22","article-title":"Characterization of MroQ-dependent maturation and export of the Staphylococcus aureus accessory gene regulatory system autoinducing peptide","volume":"90","author":"Stock","year":"2022","journal-title":"Infect. Immun."},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b0575","doi-asserted-by":"crossref","DOI":"10.1099\/mic.0.001381","article-title":"Quorum-sensing, intra-and inter-species competition in the staphylococci","volume":"169","author":"Williams","year":"2023","journal-title":"Microbiology"},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b0580","doi-asserted-by":"crossref","first-page":"e00195","DOI":"10.1128\/jb.00195-24","article-title":"Determinants of maturation of the Staphylococcus aureus autoinducing peptide","volume":"206","author":"Fang","year":"2024","journal-title":"J. Bacteriol."},{"key":"10.1016\/j.bspc.2025.109285_b0585","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s11693-013-9105-7","article-title":"A quantitative study of gene regulatory pathways in Bacillus subtilis for virulence and competence phenotype by quorum sensing","volume":"7","author":"Kumar","year":"2013","journal-title":"Syst. Synth. Biol."},{"issue":"50","key":"10.1016\/j.bspc.2025.109285_b0590","doi-asserted-by":"crossref","first-page":"21027","DOI":"10.1073\/pnas.0912185106","article-title":"Deciding fate in adverse times: sporulation and competence in Bacillus subtilis","volume":"106","author":"Schultz","year":"2009","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.bspc.2025.109285_b0595","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12866-017-1107-2","article-title":"Regulation of bacteria population behaviors by AI-2 \u201cconsumer cells\u201d and \u201csupplier cells\u201d","volume":"17","author":"Quan","year":"2017","journal-title":"BMC Microbiol."},{"key":"10.1016\/j.bspc.2025.109285_b0600","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijfoodmicro.2023.110102","article-title":"Enhancing the AI-2\/LuxS quorum sensing system in Lactiplantibacillus plantarum: effect on the elimination of biofilms grown on seafoods","volume":"389","author":"Qian","year":"2023","journal-title":"Int. J. Food Microbiol."},{"key":"10.1016\/j.bspc.2025.109285_b0605","doi-asserted-by":"crossref","unstructured":"Q. Xiong et al., \u201cQuorum sensing signal autoinducer-2 inhibits sporulation of Bacillus by interacting with RapC and functions across species,\u201d bioRxiv, p. 2021.11. 02.466875, 2021.","DOI":"10.1101\/2021.11.02.466875"},{"issue":"5\u20136","key":"10.1016\/j.bspc.2025.109285_b0610","article-title":"Synthesis and potential of Autoinducer\u20102 and Analogs to Manipulate Inter\u2010Species Quorum Sensing","volume":"63","author":"Rodrigues","year":"2023","journal-title":"Isr. J. Chem."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b0615","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1038\/s41579-019-0186-5","article-title":"Bacterial quorum sensing in complex and dynamically changing environments","volume":"17","author":"Mukherjee","year":"2019","journal-title":"Nat. Rev. Microbiol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0620","doi-asserted-by":"crossref","first-page":"5521","DOI":"10.1038\/s41467-020-19432-2","article-title":"Developing a pathway-independent and full-autonomous global resource allocation strategy to dynamically switching phenotypic states","volume":"11","author":"Wu","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b0625","article-title":"Environmental sensing in dynamic quorum responses","author":"Chu","year":"2019","journal-title":"BioRxiv"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b0630","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1021\/acssynbio.6b00177","article-title":"Autoinduced AND gate controls metabolic pathway dynamically in response to microbial communities and cell physiological state","volume":"6","author":"He","year":"2017","journal-title":"ACS Synth. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b0635","doi-asserted-by":"crossref","unstructured":"D. A. Tuan et al., \u201cHarnessing Quorum Sensing for Advanced Biotechnological Applications: Intra-and Inter-Species Communication for Synthetic Biology and Disease Control,\u201d 2024.","DOI":"10.20944\/preprints202409.1752.v1"},{"key":"10.1016\/j.bspc.2025.109285_b0640","doi-asserted-by":"crossref","unstructured":"M. Fondi, F. Di Patti, and E. Perrin, \u201cThe acquisition of additional feedback loops may optimize and speed up the response of quorum sensing,\u201d bioRxiv, p. 2021.06. 11.448020, 2021.","DOI":"10.1101\/2021.06.11.448020"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0645","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/LCSYS.2018.2871663","article-title":"Improving orthogonality in two-component biological signalling systems using feedback control","volume":"3","author":"Steel","year":"2018","journal-title":"IEEE Control Syst. Lett."},{"key":"10.1016\/j.bspc.2025.109285_b0650","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3389\/fbioe.2019.00080","article-title":"Engineered orthogonal quorum sensing systems for synthetic gene regulation in Escherichia coli","volume":"7","author":"Tekel","year":"2019","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0655","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1111\/j.1462-2920.2009.02049.x","article-title":"Identification and characterization of new LuxR\/LuxI\u2010type quorum sensing systems from metagenomic libraries","volume":"12","author":"Hao","year":"2010","journal-title":"Environ. Microbiol."},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b0660","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1093\/molbev\/msh097","article-title":"The evolutionary history of quorum-sensing systems in bacteria","volume":"21","author":"Lerat","year":"2004","journal-title":"Mol. Biol. Evol."},{"key":"10.1016\/j.bspc.2025.109285_b0665","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1134\/S0026261706040047","article-title":"Quorum-sensing regulation of gene expression: fundamental and applied aspects and the role in bacterial communication","volume":"75","author":"Khmel","year":"2006","journal-title":"Microbiology"},{"key":"10.1016\/j.bspc.2025.109285_b0670","article-title":"\u201cHybrid Quorum Sensing and Machine Learning Systems for Adaptive Synthetic Biology","author":"Tuan","year":"2024","journal-title":"Toward Autonomous Gene Regulation and Precision Therapies"},{"key":"10.1016\/j.bspc.2025.109285_b0675","series-title":"Biological Insights of Multi-Omics Technologies in Human Diseases","first-page":"311","article-title":"\u201cMachine learning approaches for multiomics data analysis","author":"Firdous","year":"2024"},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b0680","doi-asserted-by":"crossref","first-page":"9921","DOI":"10.1021\/acsomega.3c05913","article-title":"Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges","volume":"9","author":"Goshisht","year":"2024","journal-title":"ACS Omega"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0685","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.1038\/s41467-024-46203-0","article-title":"Machine learning-aided design and screening of an emergent protein function in synthetic cells","volume":"15","author":"Kohyama","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b0690","doi-asserted-by":"crossref","DOI":"10.1016\/j.jaap.2024.106512","article-title":"Exploring machine learning applications in chemical production through valorization of biomass, plastics, and petroleum resources: a comprehensive review","author":"Mafat","year":"2024","journal-title":"J. Anal. Appl. Pyrol."},{"key":"10.1016\/j.bspc.2025.109285_b0695","doi-asserted-by":"crossref","unstructured":"S. K. T. Vaishali Jain, \u201cOverview: machine learning,\u201d. In: Machine Learning: An Art of Computer Thinking, vol. 3. IIP Series, 2024, pp. 130-144.","DOI":"10.58532\/nbennurch183"},{"key":"10.1016\/j.bspc.2025.109285_b0700","doi-asserted-by":"crossref","unstructured":"A. Munde, \u201cThe Machine Learning Pipeline,\u201d. In: Deep Learning Concepts in Operations Research: Auerbach Publications, 2024, p. 18.","DOI":"10.1201\/9781003433309-18"},{"key":"10.1016\/j.bspc.2025.109285_b0705","doi-asserted-by":"crossref","unstructured":"Y. C. A. Padmanabha Reddy and N. Mohan Krishna Varma, \u201cReview on Supervised Learning Techniques,\u201d Singapore, 2020: Springer Singapore, in Emerging Research in Data Engineering Systems and Computer Communications, pp. 577-587.","DOI":"10.1007\/978-981-15-0135-7_53"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0710","doi-asserted-by":"crossref","DOI":"10.1002\/cppb.20106","article-title":"Supervised learning of gene regulatory networks","volume":"5","author":"Razaghi-Moghadam","year":"2020","journal-title":"Current Protocols in Plant Biology"},{"key":"10.1016\/j.bspc.2025.109285_b0715","series-title":"Supervised Machine Learning,\u201c in Materials Informatics and Catalysts Informatics: an Introduction","first-page":"191","author":"Takahashi","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b0720","unstructured":"A. R. Campa\u00f1a, J. T. Bacete, and J. N. Enrique, \u201cEngineering self-regulated synthetic gene expression systems and its application on controlled protein expression,\u201d Biosaia: Revista de los m\u00e1steres de Biotecnolog\u00eda Sanitaria y Biotecnolog\u00eda Ambiental, Industrial y Alimentaria, no. 8, 2019."},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b0725","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004781","article-title":"Quorum sensing desynchronization leads to bimodality and patterned behaviors","volume":"12","author":"Quan","year":"2016","journal-title":"PLoS Comput. Biol."},{"issue":"38","key":"10.1016\/j.bspc.2025.109285_b0730","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.21105\/joss.01087","article-title":"ReinforcementLearning: a package to perform model-free reinforcement learning in R","volume":"4","author":"Pr\u00f6ellochs","year":"2019","journal-title":"J. Open Source Software"},{"key":"10.1016\/j.bspc.2025.109285_b0735","series-title":"2022 IEEE XXIX International Conference on Electronics, Electrical Engineering and Computing (INTERCON)","first-page":"1","article-title":"A noise-insensitive reinforcement learning control for a nonlinear bioreactor","author":"Estrada-Rayme","year":"2022"},{"issue":"11","key":"10.1016\/j.bspc.2025.109285_b0740","article-title":"Combined oxygen and glucose oscillations distinctly change the transcriptional and physiological state of Escherichia coli","volume":"17","author":"Bafna-R\u00fchrer","year":"2024","journal-title":"J. Microbial. Biotechnol."},{"issue":"46","key":"10.1016\/j.bspc.2025.109285_b0745","doi-asserted-by":"crossref","first-page":"18382","DOI":"10.1021\/acs.est.3c00353","article-title":"Systematic performance evaluation of reinforcement learning algorithms applied to wastewater treatment control optimization","volume":"57","author":"Croll","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.bspc.2025.109285_b0750","doi-asserted-by":"crossref","unstructured":"\u201cUnsupervised Methods,\u201d in Multiblock Data Fusion in Statistics and Machine Learning, 2022, pp. 113-165.","DOI":"10.1002\/9781119600978.ch5"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0755","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1038\/s41467-022-30741-6","article-title":"Machine learning aided construction of the quorum sensing communication network for human gut microbiota","volume":"13","author":"Wu","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b0760","doi-asserted-by":"crossref","unstructured":"D. Saligkaras and V. E. Papageorgiou, \u201cSeeking the truth beyond the data. An unsupervised machine learning approach,\u201d. In: AIP Conference Proceedings, 2023, vol. 2812, no. 1: AIP Publishing.","DOI":"10.1063\/5.0161454"},{"key":"10.1016\/j.bspc.2025.109285_b0765","doi-asserted-by":"crossref","unstructured":"U. R. Hodeghatta and U. Nayak, \u201cUnsupervised Machine Learning,\u201d. In: Business Analytics Using R - A Practical Approach. Berkeley, CA: Apress, 2017, pp. 161-186.","DOI":"10.1007\/978-1-4842-2514-1_7"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b0770","doi-asserted-by":"crossref","DOI":"10.1088\/1478-3975\/aa7c1e","article-title":"Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities","volume":"14","author":"Yusufaly","year":"2017","journal-title":"Phys. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b0775","series-title":"2015 49th Asilomar Conference on Signals, Systems and Computers","first-page":"133","article-title":"A stochastic queuing model of quorum sensing in microbial communities","author":"Michelusi","year":"2015"},{"key":"10.1016\/j.bspc.2025.109285_b0780","doi-asserted-by":"crossref","unstructured":"P. Das, D. Vetrithangam, G. H. Krishnan, A. Kumar, and R. V. K. Reddy, \u201cDeep Learning Techniques Revolutionizing Biomedical Applications: Arrhythmia Detection, Cardiac Sensed Signals, and Cell-Free Synthetic Biology,\u201d. In: Applications of Synthetic Biology in Health, Energy, and Environment: IGI Global, 2023, pp. 68-91.","DOI":"10.4018\/978-1-6684-6577-6.ch004"},{"key":"10.1016\/j.bspc.2025.109285_b0785","unstructured":"S. Kumar et al., \u201cDeep learning in computational biology: Advancements, challenges, and future outlook,\u201d arXiv preprint arXiv:2310.03086, 2023."},{"key":"10.1016\/j.bspc.2025.109285_b0790","series-title":"Deep Learning Concepts in Operations Research","first-page":"27","article-title":"Deep Learning is a State-of-the-Art Approach to Artificial Intelligence","author":"Mohanta","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b0795","doi-asserted-by":"crossref","unstructured":"R. Ravanmehr and R. Mohamadrezaei, \u201cDeep Learning Overview,\u201d. In: Session-Based Recommender Systems Using Deep Learning. Cham: Springer Nature Switzerland, 2024, pp. 27-72.","DOI":"10.1007\/978-3-031-42559-2_2"},{"key":"10.1016\/j.bspc.2025.109285_b0800","series-title":"Deep Learning Concepts in Operations Research","first-page":"9","article-title":"Deep Learning Impacts in the Field of Artificial Intelligence","author":"Shafik","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0805","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1038\/s41467-024-46486-3","article-title":"Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment","volume":"15","author":"Choe","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b0810","doi-asserted-by":"crossref","DOI":"10.3389\/fdata.2023.1140663","article-title":"Unsupervised domain adaptation methods for cross-species transfer of regulatory code signals","volume":"6","author":"Latyshev","year":"2023","journal-title":"Front. Big Data"},{"key":"10.1016\/j.bspc.2025.109285_b0815","article-title":"Transfer learning and domain adaptation in deep networks","author":"Shamreen Ahamed","year":"2023","journal-title":"Jupiter Publications Consortium"},{"key":"10.1016\/j.bspc.2025.109285_b0820","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkae429","article-title":"Species-specific design of artificial promoters by transfer-learning based generative deep-learning model","author":"Xia","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.bspc.2025.109285_b0825","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2023.104887","article-title":"A locally weighted, correlated subdomain adaptive network employed to facilitate transfer learning","volume":"141","author":"Xu","year":"2024","journal-title":"Image Vis. Comput."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0830","first-page":"95","article-title":"Transfer learning by reusing structured knowledge","volume":"32","author":"Yang","year":"2011","journal-title":"AI Mag."},{"key":"10.1016\/j.bspc.2025.109285_b0835","unstructured":"K. Bou\u010dek, \u201cMehanizmi mno\u017einske samoregulacije klica i stvaranja biofilma u etiopatogenezi infektivnih bolesti,\u201d University of Zagreb. School of Medicine. Chair of Pathophysiology, 2016."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0840","doi-asserted-by":"crossref","DOI":"10.1128\/msystems.01058-21","article-title":"Revealing general patterns of microbiomes that transcend systems: potential and challenges of deep transfer learning","volume":"7","author":"David","year":"2022","journal-title":"Msystems"},{"key":"10.1016\/j.bspc.2025.109285_b0845","series-title":"Autonomous Mobile Robots","first-page":"715","article-title":"Chapter 15 - an introduction to evolutionary computation","author":"Kala","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b0850","doi-asserted-by":"crossref","unstructured":"R. Kruse, S. Mostaghim, C. Borgelt, C. Braune, and M. Steinbrecher, \u201cIntroduction to Evolutionary Algorithms,\u201d. In: Computational Intelligence: A Methodological Introduction. Cham: Springer International Publishing, 2022, pp. 225-254.","DOI":"10.1007\/978-3-030-42227-1_11"},{"key":"10.1016\/j.bspc.2025.109285_b0855","doi-asserted-by":"crossref","unstructured":"L. Liu, T. Fei, Z. Zhu, K. Wu, and Y. Zhang, \u201cA Survey of Evolutionary Algorithms,\u201d. In: 2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 25-27 Aug. 2023 2023, pp. 22-27, doi: 10.1109\/ICBAIE59714.2023.10281260.","DOI":"10.1109\/ICBAIE59714.2023.10281260"},{"key":"10.1016\/j.bspc.2025.109285_b0860","series-title":"Building Energy Management Systems and Techniques","first-page":"111","article-title":"Chapter 7 - Evolutionary optimization","author":"Luo","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b0865","series-title":"2021 IEEE Congress on Evolutionary Computation (CEC)","first-page":"127","article-title":"Improving evolutionary algorithms by enhancing an approximative fitness function through prediction intervals","author":"Plump","year":"2021"},{"key":"10.1016\/j.bspc.2025.109285_b0870","article-title":"A study on evolutionary algorithms and its applications","volume":"1","author":"Pon Bharathi","year":"2022","journal-title":"Electrical and Automation Engineering."},{"key":"10.1016\/j.bspc.2025.109285_b0875","series-title":"Genetic Algorithms","article-title":"Introduction to Evolutionary Algorithms","author":"Tamilselvi","year":"2022"},{"key":"10.1016\/j.bspc.2025.109285_b0880","series-title":"Frontiers in Genetics Algorithm Theory and Applications","first-page":"3","article-title":"Evolutionary Computation: Perspectives on past and Future","author":"Yoshida","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b0885","series-title":"\u201cEvolutionary Computing,\u201d in Intelligent Information Processing with Matlab","first-page":"173","author":"Zhang","year":"2023"},{"key":"10.1016\/j.bspc.2025.109285_b0890","article-title":"Evolutionary Algorithms (EAs)","volume":"ch. 9","author":"Yan-Fu Li","year":"2022","journal-title":"Reliability Assessment and Optimization: Methods and Applications"},{"key":"10.1016\/j.bspc.2025.109285_b0895","doi-asserted-by":"crossref","unstructured":"B.-S. Chen, \u201cChapter 6 - Robust Design of Genetic Networks: Evolutionary Systems Biology Approach via an Evolutionary Algorithm (EA) in Phenotype Space,\u201d. In: Systems Evolutionary Biology, B.-S. Chen Ed.: Academic Press, 2018, pp. 103-121.","DOI":"10.1016\/B978-0-12-814072-7.00006-1"},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b0900","first-page":"1464","article-title":"Optimization of fermentation processes using evolutionary algorithms\u2013a review","volume":"6","author":"Adeyemo","year":"2011","journal-title":"Sci. Res. Essays"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0905","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1002\/jms.1275","article-title":"Profiling of N-acyl-homoserine lactones by liquid chromatography coupled with electrospray ionization and a hybrid quadrupole linear ion-trap and Fourier-transform ion-cyclotron-resonance mass spectrometry (LC-ESI-LTQ-FTICR-MS)","volume":"43","author":"Cataldi","year":"2008","journal-title":"J. Mass Spectrom."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0910","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s00216-010-4341-0","article-title":"Simultaneous quantitative profiling of N-acyl-l-homoserine lactone and 2-alkyl-4(1H)-quinolone families of quorum-sensing signaling molecules using LC-MS\/MS","volume":"399","author":"Ortori","year":"2011","journal-title":"Anal. Bioanal. Chem."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0915","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1128\/AEM.67.2.575-585.2001","article-title":"gfp-based N-Acyl homoserine-lactone sensor systems for detection of bacterial communication","volume":"67","author":"Andersen","year":"2001","journal-title":"Appl. Environ. Microbiol."},{"issue":"12","key":"10.1016\/j.bspc.2025.109285_b0920","doi-asserted-by":"crossref","first-page":"7046","DOI":"10.1073\/pnas.95.12.7046","article-title":"Quorum sensing in Escherichia coli and Salmonella typhimurium","volume":"95","author":"Surette","year":"1998","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b0925","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S1369-5274(03)00028-6","article-title":"LuxS quorum sensing: more than just a numbers game","volume":"6","author":"Xavier","year":"2003","journal-title":"Curr. Opin. Microbiol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0930","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: gene set variation analysis for microarray and RNA-Seq data","volume":"14","author":"H\u00e4nzelmann","year":"2013","journal-title":"BMC Bioinf."},{"issue":"43","key":"10.1016\/j.bspc.2025.109285_b0935","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"D1","key":"10.1016\/j.bspc.2025.109285_b0940","doi-asserted-by":"crossref","first-page":"D581","DOI":"10.1093\/nar\/gkt1099","article-title":"PATRIC, the bacterial bioinformatics database and analysis resource","volume":"42","author":"Wattam","year":"2013","journal-title":"Nucleic Acids Res."},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b0945","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0950","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1471-2105-7-91","article-title":"Bias in error estimation when using cross-validation for model selection","volume":"7","author":"Varma","year":"2006","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.bspc.2025.109285_b0955","first-page":"973","article-title":"The foundations of cost-sensitive learning","volume":"vol. 17, no. 1","author":"Elkan","year":"2001"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0960","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/1471-2105-14-106","article-title":"SMOTE for high-dimensional class-imbalanced data","volume":"14","author":"Blagus","year":"2013","journal-title":"BMC Bioinf."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b0965","doi-asserted-by":"crossref","first-page":"788","DOI":"10.30574\/wjarr.2024.24.1.3099","article-title":"AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling","volume":"24","author":"Akintola","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"10.1016\/j.bspc.2025.109285_b0970","article-title":"Machine learning control of environmental systems","author":"Fan","year":"2019","journal-title":"Ed: Google Patents"},{"issue":"46","key":"10.1016\/j.bspc.2025.109285_b0975","doi-asserted-by":"crossref","first-page":"18058","DOI":"10.1021\/acs.est.3c00360","article-title":"Machine learning-assisted, process-based quality control for detecting compromised environmental sensors","volume":"57","author":"Schmidt","year":"2023","journal-title":"Environ. Sci. Technol."},{"issue":"21","key":"10.1016\/j.bspc.2025.109285_b0980","doi-asserted-by":"crossref","first-page":"4100","DOI":"10.3390\/math10214100","article-title":"Machine learning feedback control approach based on symbolic regression for robotic systems","volume":"10","author":"Diveev","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.bspc.2025.109285_b0985","series-title":"International Conference on Computational Science","first-page":"576","article-title":"ML-based proactive control of industrial processes","author":"Kuk","year":"2023"},{"key":"10.1016\/j.bspc.2025.109285_b0990","doi-asserted-by":"crossref","unstructured":"A. M. Zand, S. Anastassov, T. Frei, and M. Khammash, \u201cMulti-Layer Autocatalytic Feedback Enables Integral Control Amidst Resource Competition and Across Scales,\u201d bioRxiv, p. 2024.08. 22.609155, 2024.","DOI":"10.1101\/2024.08.22.609155"},{"key":"10.1016\/j.bspc.2025.109285_b0995","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.1109\/LCSYS.2023.3287136","article-title":"Multicellular pd control in microbial consortia","volume":"7","author":"Martinelli","year":"2023","journal-title":"IEEE Control Syst. Lett."},{"key":"10.1016\/j.bspc.2025.109285_b1000","article-title":"Microbial regulation of feedbacks to ecosystem change","author":"Sveen","year":"2024","journal-title":"Trends Microbiol."},{"issue":"11","key":"10.1016\/j.bspc.2025.109285_b1005","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1109\/JAS.2023.123303","article-title":"A model predictive control Algorithm based on biological regulatory mechanism and operational research","volume":"10","author":"Yang","year":"2023","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.bspc.2025.109285_b1010","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2022.801032","article-title":"Modeling and optimization of a molecular biocontroller for the regulation of complex metabolic pathways","volume":"9","author":"Boada","year":"2022","journal-title":"Front. Mol. Biosci."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1015","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevResearch.3.033183","article-title":"Entangled gene regulatory networks with cooperative expression endow robust adaptive responses to unforeseen environmental changes","volume":"3","author":"Inoue","year":"2021","journal-title":"Phys. Rev. Res."},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b1020","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/nrg2398","article-title":"Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation","volume":"9","author":"L\u00f3pez-Maury","year":"2008","journal-title":"Nat. Rev. Genet."},{"key":"10.1016\/j.bspc.2025.109285_b1025","series-title":"2024 2nd World Conference on Communication & Computing (WCONF)","first-page":"1","article-title":"Utilising Machine Learning for Modulation and Signal Processing","author":"Singh","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b1030","unstructured":"B. Lengerich, C. N. Ellington, A. Rubbi, M. Kellis, and E. P. Xing, \u201cContextualized machine learning,\u201d arXiv preprint arXiv:2310.11340, 2023."},{"issue":"12","key":"10.1016\/j.bspc.2025.109285_b1035","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1002\/iub.2693","article-title":"Machine learning applications for transcription level and phenotype predictions","volume":"74","author":"Chantaraamporn","year":"2022","journal-title":"IUBMB Life"},{"key":"10.1016\/j.bspc.2025.109285_b1040","doi-asserted-by":"crossref","unstructured":"S. Drusinsky, S. Whalen, and K. S. Pollard, \u201cDeep-learning prediction of gene expression from personal genomes,\u201d bioRxiv, p. 2024.07. 27.605449, 2024.","DOI":"10.1101\/2024.07.27.605449"},{"issue":"11","key":"10.1016\/j.bspc.2025.109285_b1045","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.3390\/life14111490","article-title":"Machine learning-based software for predicting pseudomonas spp. growth dynamics in culture media","volume":"14","author":"Tarlak","year":"2024","journal-title":"Life"},{"issue":"21","key":"10.1016\/j.bspc.2025.109285_b1050","doi-asserted-by":"crossref","first-page":"7467","DOI":"10.1021\/jm901742e","article-title":"Medicinal chemistry as a conduit for the modulation of quorum sensing","volume":"53","author":"Lowery","year":"2010","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.bspc.2025.109285_b1055","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1128\/mBio.01863-16","article-title":"Quorum sensing coordinates cooperative expression of pyruvate metabolism genes to maintain a sustainable environment for population stability","volume":"7","author":"Hawver","year":"2016","journal-title":"MBio"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b1060","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1002\/bit.26080","article-title":"Constructing \u201cquantized quorums\u201d to guide emergent phenotypes through quorum quenching capsules","volume":"114","author":"Zargar","year":"2017","journal-title":"Biotechnol. Bioeng."},{"key":"10.1016\/j.bspc.2025.109285_b1065","doi-asserted-by":"crossref","unstructured":"G. Singh, \u201cENVIRONMENTAL MONITORING WITH MACHINE LEARNING,\u201d EPRA International Journal of Multidisciplinary Research (IJMR), 9 (5), 208, vol. 212, 2023.","DOI":"10.36713\/epra13330"},{"key":"10.1016\/j.bspc.2025.109285_b1070","doi-asserted-by":"crossref","unstructured":"A. Amato, V. Di Lecce, and V. Piuri, \u201cDistributed database for environmental data integration,\u201d. In: 2007 IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2007: IEEE, pp. 47-51.","DOI":"10.1109\/VECIMS.2007.4373926"},{"key":"10.1016\/j.bspc.2025.109285_b1075","doi-asserted-by":"crossref","unstructured":"Y. Li and A. Ngom, \u201cData integration in machine learning,\u201d. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015: IEEE, pp. 1665-1671.","DOI":"10.1109\/BIBM.2015.7359925"},{"key":"10.1016\/j.bspc.2025.109285_b1080","article-title":"Integrating machine learning in metabolomics: a path to enhanced diagnostics and data interpretation","author":"Xu","year":"2024","journal-title":"Small Methods"},{"key":"10.1016\/j.bspc.2025.109285_b1085","unstructured":"A. C. Society, Microbial Stress Response: Mechanisms and Data Science. ACS Publications, 2023."},{"key":"10.1016\/j.bspc.2025.109285_b1090","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/B978-0-12-817558-3.00003-2","article-title":"Stress responses modulate bacterial competitive fitness in polymicrobial communities","author":"Tang","year":"2024","journal-title":"Stress Immunology and Inflammation: Elsevier"},{"key":"10.1016\/j.bspc.2025.109285_b1095","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2180-12-190","article-title":"The effect of environmental conditions on expression of Bacteroides fragilis and Bacteroides thetaiotaomicron C10 protease genes","volume":"12","author":"Thornton","year":"2012","journal-title":"BMC Microbiol."},{"issue":"12","key":"10.1016\/j.bspc.2025.109285_b1100","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1004872","article-title":"Mutations in global regulators lead to metabolic selection during adaptation to complex environments","volume":"10","author":"Saxer","year":"2014","journal-title":"PLoS Genet."},{"key":"10.1016\/j.bspc.2025.109285_b1105","unstructured":"K. Gopinathan and S. Rangarajan, \u201cReal-Time Adaptive Decision System And Method Using Predictive Modeling,\u201d ed: Google Patents, 2015."},{"key":"10.1016\/j.bspc.2025.109285_b1110","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.ymben.2024.02.012","article-title":"COSMIC-dFBA: a novel multi-scale hybrid framework for bioprocess modeling","volume":"82","author":"Gopalakrishnan","year":"2024","journal-title":"Metab. Eng."},{"issue":"10","key":"10.1016\/j.bspc.2025.109285_b1115","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1002\/ceat.202200029","article-title":"Fermentation process control and optimization","volume":"45","author":"Chai","year":"2022","journal-title":"Chem. Eng. Technol."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b1120","doi-asserted-by":"crossref","first-page":"142","DOI":"10.63053\/ijhes.81","article-title":"Biomanufacturing for a Sustainable Future: Unleashing the potential of Biotechnology in Pharmaceutical Raw Material Production","volume":"2","author":"Shokoohi","year":"2024","journal-title":"Int. J. New Findings in Health and Educational Sciences (IJHES)"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1125","doi-asserted-by":"crossref","DOI":"10.1093\/ismeco\/ycae018","article-title":"Constructions of quorum sensing signaling network for activated sludge microbial community","volume":"4","author":"Jin","year":"2024","journal-title":"ISME Commun."},{"key":"10.1016\/j.bspc.2025.109285_b1130","article-title":"Microbial consortium degrading of organic pollutants: source, degradation efficiency, pathway, mechanism and application","author":"L\u00fc","year":"2024","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.bspc.2025.109285_b1135","doi-asserted-by":"crossref","DOI":"10.3389\/fmed.2023.1227168","article-title":"Exploring alternative approaches to precision medicine through genomics and artificial intelligence\u2013a systematic review","volume":"10","author":"Mumtaz","year":"2023","journal-title":"Front. Med."},{"key":"10.1016\/j.bspc.2025.109285_b1140","doi-asserted-by":"crossref","unstructured":"A. R. Kahkoska, N. L. Freeman, and K. H. Lich, \u201cSystems-aligned precision medicine\u2014building an evidence base for individuals within complex systems,\u201d. In: JAMA health forum, 2022, vol. 3, no. 7: American Medical Association, pp. e222334.","DOI":"10.1001\/jamahealthforum.2022.2334"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b1145","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1139\/gen-2020-0131","article-title":"Machine learning for precision medicine","volume":"64","author":"MacEachern","year":"2021","journal-title":"Genome"},{"key":"10.1016\/j.bspc.2025.109285_b1150","doi-asserted-by":"crossref","DOI":"10.36783\/18069657rbcs20210098","article-title":"Soil-plant-microbiota interactions to enhance plant growth","volume":"46","author":"Volpiano","year":"2022","journal-title":"Revista Brasileira De Ci\u00eancia Do Solo"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1155","first-page":"225","article-title":"Plant-microbe interaction: mechanisms and applications for improving crop yield and quality","volume":"17","author":"Makar","year":"2023","journal-title":"\u0411io\u043bo\u0433i\u0447\u043di C\u0442y\u0434i\u0457\/studia Biologica"},{"key":"10.1016\/j.bspc.2025.109285_b1160","doi-asserted-by":"crossref","DOI":"10.1016\/j.copbio.2024.103150","article-title":"Engineering plant\u2013microbe communication for plant nutrient use efficiency","volume":"88","author":"Griffin","year":"2024","journal-title":"Curr. Opin. Biotechnol."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1165","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.copbio.2008.05.003","article-title":"Importance of systems biology in engineering microbes for biofuel production","volume":"19","author":"Mukhopadhyay","year":"2008","journal-title":"Curr. Opin. Biotechnol."},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b1170","doi-asserted-by":"crossref","first-page":"803","DOI":"10.3390\/bioengineering11080803","article-title":"Enhancing fermentation process monitoring through data-driven modeling and synthetic time series generation","volume":"11","author":"Kwon","year":"2024","journal-title":"Bioengineering"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1175","doi-asserted-by":"crossref","first-page":"79","DOI":"10.6000\/1927-3037.2014.03.03.1","article-title":"From beverages to biofuels: the journeys of ethanol-producing microorganisms","volume":"3","author":"Macedo","year":"2014","journal-title":"Int. J. Biotechnology for Wellness Industries"},{"key":"10.1016\/j.bspc.2025.109285_b1180","doi-asserted-by":"crossref","unstructured":"A. Alsiyabi, S. A. Shahid, and A. Al-Harrasi, \u201cAn Automated Machine Learning Framework for Antimicrobial Resistance Prediction Through Transcriptomics,\u201d bioRxiv, p. 2024.06. 22.600223, 2024.","DOI":"10.1101\/2024.06.22.600223"},{"key":"10.1016\/j.bspc.2025.109285_b1185","doi-asserted-by":"crossref","unstructured":"C. Gotti et al., \u201cLC-SRM combined with machine learning enables fast identification and quantification of bacterial pathogens in urinary tract infections,\u201d bioRxiv, p. 2024.05. 31.596829, 2024.","DOI":"10.1101\/2024.05.31.596829"},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b1190","doi-asserted-by":"crossref","first-page":"788","DOI":"10.3390\/antibiotics13080788","article-title":"Artificial intelligence-driven analysis of antimicrobial-resistant and biofilm-forming pathogens on biotic and abiotic surfaces","volume":"13","author":"Mishra","year":"2024","journal-title":"Antibiotics"},{"key":"10.1016\/j.bspc.2025.109285_b1195","doi-asserted-by":"crossref","DOI":"10.1002\/asia.202400102","article-title":"Applying machine learning for antibiotic development and prediction of microbial resistance","author":"Panjla","year":"2024","journal-title":"Chem\u2013an Asian J."},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b1200","doi-asserted-by":"crossref","first-page":"140","DOI":"10.3390\/chemosensors12070140","article-title":"Machine learning-assisted Raman spectroscopy and SERS for bacterial pathogen detection: clinical, food safety, and environmental applications","volume":"12","author":"Rahman","year":"2024","journal-title":"Chemosensors"},{"key":"10.1016\/j.bspc.2025.109285_b1205","article-title":"Artificial intelligence applications in the diagnosis and treatment of bacterial infections","volume":"15","author":"Zhang","year":"2024","journal-title":"Front. Microbiol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1210","doi-asserted-by":"crossref","DOI":"10.1080\/21655979.2023.2243416","article-title":"A data-driven machine learning approach for discovering potent LasR inhibitors","volume":"14","author":"Koh","year":"2023","journal-title":"Bioengineered"},{"key":"10.1016\/j.bspc.2025.109285_b1215","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ymben.2023.03.001","article-title":"Optogenetic closed-loop feedback control of the unfolded protein response optimizes protein production","volume":"77","author":"Benisch","year":"2023","journal-title":"Metab. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1220","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1038\/s41467-024-46361-1","article-title":"Deep model predictive control of gene expression in thousands of single cells","volume":"15","author":"Lugagne","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b1225","doi-asserted-by":"crossref","first-page":"309","DOI":"10.3390\/info16040309","article-title":"AI-Based Detection of Optical Microscopic Images of Pseudomonas aeruginosa in Planktonic and Biofilm States","volume":"16","author":"Sengupta","year":"2025","journal-title":"Information"},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b1230","doi-asserted-by":"crossref","DOI":"10.1016\/j.sjbs.2024.104001","article-title":"Computational and experimental strategies for combating MBL P. aeruginosa (MBLPA) biofilms using phytochemicals: targeting the quorum sensing network","volume":"31","author":"Fakhar","year":"2024","journal-title":"Saudi J. Biological Sci."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1235","doi-asserted-by":"crossref","DOI":"10.1080\/14756366.2024.2330907","article-title":"Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR","volume":"39","author":"Galvez-Llompart","year":"2024","journal-title":"J. Enzyme Inhib. Med. Chem."},{"key":"10.1016\/j.bspc.2025.109285_b1240","doi-asserted-by":"crossref","DOI":"10.1016\/j.micpath.2024.106609","article-title":"Attenuation of Las\/Rhl quorum sensing regulated virulence and biofilm formation in Pseudomonas aeruginosa PAO1 by Artocarpesin","volume":"189","author":"Mohan","year":"2024","journal-title":"Microb. Pathog."},{"key":"10.1016\/j.bspc.2025.109285_b1245","doi-asserted-by":"crossref","unstructured":"S. Munagala et al., \u201cIntegrating QQ with Nano-techniques\u2013A Potent Antibacterial Therapy,\u201d 2023.","DOI":"10.1039\/BK9781837671380-00368"},{"key":"10.1016\/j.bspc.2025.109285_b1250","first-page":"1","article-title":"Computational assessment of marine natural products as LasR inhibitors for attenuating quorum sensing in Pseudomonas aeruginosa","author":"Singothu","year":"2024","journal-title":"J. Biomol. Struct. Dyn."},{"key":"10.1016\/j.bspc.2025.109285_b1255","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.16476","article-title":"Whole-transcriptome analysis reveals mechanisms underlying antibacterial activity and biofilm inhibition by a malic acid combination (MAC) in Pseudomonas aeruginosa","volume":"11","author":"Song","year":"2023","journal-title":"PeerJ"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1260","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1007\/s00253-024-13247-7","article-title":"Effect of L-HSL on biofilm and motility of Pseudomonas aeruginosa and its mechanism","volume":"108","author":"Tang","year":"2024","journal-title":"Appl. Microbiol. Biotechnol."},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b1265","doi-asserted-by":"crossref","first-page":"302","DOI":"10.2174\/011573398X283365240208195944","article-title":"The future of cystic fibrosis care: exploring AI's Impact on detection and therapy","volume":"20","author":"Basu","year":"2024","journal-title":"Current Respiratory Medicine Rev."},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b1270","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011424","article-title":"Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis","volume":"19","author":"Gra\u00f1a-Miraglia","year":"2023","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b1275","doi-asserted-by":"crossref","first-page":"3861","DOI":"10.2147\/IJN.S445955","article-title":"Enzyme-linked lipid nanocarriers for coping pseudomonal pulmonary infection. Would nanocarriers complement biofilm disruption or pave its road?","author":"Nafee","year":"2024","journal-title":"Int. J. Nanomed."},{"key":"10.1016\/j.bspc.2025.109285_b1280","article-title":"Therapy of gram-negative infection in the complex treatment of cystic fibrosis","volume":"33","author":"Voronkova","year":"2023","journal-title":"Russian Pulmonology J."},{"key":"10.1016\/j.bspc.2025.109285_b1285","doi-asserted-by":"crossref","unstructured":"J. Chung, S. Eisha, S. Park, A. Morris, and I. Martin, \u201cTargeting Matrix Exopolysaccharides to Disrupt Pseudomonas aeruginosa Biofilms in Cystic Fibrosis,\u201d 2023.","DOI":"10.20944\/preprints202302.0042.v1"},{"issue":"49","key":"10.1016\/j.bspc.2025.109285_b1290","article-title":"Cystic fibrosis and pulmonary biofilms","volume":"11","author":"Kenneth","year":"2023","journal-title":"The Southwest Respiratory and Critical Care Chronicles"},{"key":"10.1016\/j.bspc.2025.109285_b1295","doi-asserted-by":"crossref","unstructured":"U. Rappo et al., \u201cLate Breaking Abstract-A novel treatment for chronic P. aeruginosa pulmonary infection in CF subjects-A phase 1b\/2a randomized, double-blind, placebo-controlled, multicenter study to evaluate phage therapy,\u201d ed: Eur Respiratory Soc, 2023.","DOI":"10.1183\/13993003.congress-2023.OA1534"},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b1300","first-page":"692","article-title":"Improving industrial quality control by machine learning techniques","volume":"5","author":"Alzaidi","year":"2024","journal-title":"J. La Multiapp"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1305","doi-asserted-by":"crossref","DOI":"10.1080\/19420889.2024.2415598","article-title":"Quorum sensing and antibiotic resistance in polymicrobial infections","volume":"17","author":"Cui","year":"2024","journal-title":"Commun. Integr. Biol."},{"issue":"11","key":"10.1016\/j.bspc.2025.109285_b1310","doi-asserted-by":"crossref","first-page":"79","DOI":"10.9734\/ajrid\/2024\/v15i11394","article-title":"Exploring the role of artificial intelligence in revolutionizing microbial diagnostics","volume":"15","author":"Elshafei","year":"2024","journal-title":"Asian J. Res. Infectious Diseases"},{"key":"10.1016\/j.bspc.2025.109285_b1315","doi-asserted-by":"crossref","DOI":"10.3389\/fmicb.2024.1474078","article-title":"Application of machine learning based genome sequence analysis in pathogen identification","volume":"15","author":"Gao","year":"2024","journal-title":"Front. Microbiol."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b1320","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1515\/dmpt-2024-0003","article-title":"The synergy of artificial intelligence and personalized medicine for the enhanced diagnosis, treatment, and prevention of disease","volume":"39","author":"Zahra","year":"2024","journal-title":"Drug Metabolism and Personalized Therapy"},{"key":"10.1016\/j.bspc.2025.109285_b1325","doi-asserted-by":"crossref","unstructured":"A. Anju, R. Kanthavel, and K. Venket, \u201cPersonalized Treatment and Patient Care Using AI,\u201d. In: AI for Large Scale Communication Networks: IGI Global, 2025, pp. 289-302.","DOI":"10.4018\/979-8-3693-6552-6.ch013"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b1330","first-page":"215","article-title":"Bacterial quorum sensing: functional features and potential applications in biotechnology","volume":"22","author":"Mangwani","year":"2012","journal-title":"J. Mol. Microbiol. Biotechnol."},{"key":"10.1016\/j.bspc.2025.109285_b1335","series-title":"Annales Pharmaceutiques Fran\u00e7aises","first-page":"413","article-title":"Des enzymes pour bloquer la communication bact\u00e9rienne, une alternative aux antibiotiques?","author":"R\u00e9my","year":"2016"},{"issue":"18","key":"10.1016\/j.bspc.2025.109285_b1340","doi-asserted-by":"crossref","first-page":"9821","DOI":"10.1093\/nar\/gkad667","article-title":"tRNA queuosine modification is involved in biofilm formation and virulence in bacteria","volume":"51","author":"D\u00edaz-Rullo","year":"2023","journal-title":"Nucleic Acids Res."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b1345","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1021\/acs.jcim.4c00087","article-title":"Machine learning-based virtual screening of antibacterial agents against methicillin-susceptible and resistant staphylococcus aureus","volume":"64","author":"Fernandes","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1350","doi-asserted-by":"crossref","first-page":"2036","DOI":"10.1038\/s41467-024-46211-0","article-title":"Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records","volume":"15","author":"Nigo","year":"2024","journal-title":"Nat. Commun."},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b1355","doi-asserted-by":"crossref","first-page":"842","DOI":"10.3390\/microorganisms12050842","article-title":"Tackling the antimicrobial resistance \u201cpandemic\u201d with machine learning tools: a summary of available evidence","volume":"12","author":"Rusic","year":"2024","journal-title":"Microorganisms"},{"key":"10.1016\/j.bspc.2025.109285_b1360","doi-asserted-by":"crossref","DOI":"10.1109\/TCBB.2024.3434340","article-title":"Machine learning-assisted high-throughput screening for Anti-MRSA compounds","author":"Shehadeh","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.bspc.2025.109285_b1365","doi-asserted-by":"crossref","DOI":"10.1016\/j.bbrc.2024.149912","article-title":"Eliminating extracellular autoinducing peptide signals inhibits the Staphylococcus aureus quorum sensing agr system","volume":"711","author":"Inagaki","year":"2024","journal-title":"Biochem. Biophys. Res. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b1370","doi-asserted-by":"crossref","unstructured":"L. Zhang, Y. Xu, M. Wu, L. Wang, and H. Xu, \u201cQuantum Long Short-Term Memory for Drug Discovery,\u201d arXiv preprint arXiv:2407.19852, 2024.","DOI":"10.21203\/rs.3.rs-4967201\/v1"},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b1375","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijantimicag.2024.107142","article-title":"Impact of the implementation of the Intelligent Antimicrobial System (iAMS) on clinical outcomes among patients with bacteraemia caused by methicillin-resistant Staphylococcus aureus","volume":"63","author":"Ho","year":"2024","journal-title":"Int. J. Antimicrob. Agents"},{"issue":"10","key":"10.1016\/j.bspc.2025.109285_b1380","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0163966","article-title":"Monitoring in real time the formation and removal of biofilms from clinical related pathogens using an impedance-based technology","volume":"11","author":"Guti\u00e9rrez","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2025.109285_b1385","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s13206-016-0310-9","article-title":"Quorum sensing is crucial to Escherichia coli O157: H7 biofilm formation under static or very slow laminar flow conditions","volume":"10","author":"Lim","year":"2016","journal-title":"BioChip J."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b1390","doi-asserted-by":"crossref","DOI":"10.1177\/00368504231223029","article-title":"Optical sensing for real-time detection of food-borne pathogens in fresh produce using machine learning","volume":"107","author":"Sharma","year":"2024","journal-title":"Sci. Prog."},{"key":"10.1016\/j.bspc.2025.109285_b1395","doi-asserted-by":"crossref","unstructured":"P. K. Yadav et al., \u201cDetection of E. coli concentration levels using CSI-D+ handheld with UV-C fluorescence imaging and deep learning on leaf surfaces,\u201d. In: Sensing for Agriculture and Food Quality and Safety XVI, 2024, vol. 13060: SPIE, pp. 44-54.","DOI":"10.1117\/12.3014017"},{"key":"10.1016\/j.bspc.2025.109285_b1400","doi-asserted-by":"crossref","DOI":"10.1016\/j.bios.2024.116338","article-title":"Chip-based automated equipment for dual-mode point-of-care testing foodborne pathogens","volume":"257","author":"Yin","year":"2024","journal-title":"Biosens. Bioelectron."},{"key":"10.1016\/j.bspc.2025.109285_b1405","series-title":"International Conference on Information Science and Applications","first-page":"171","article-title":"Real time food monitoring and quality alert system using IoT and streamlit","author":"Pramanik","year":"2023"},{"key":"10.1016\/j.bspc.2025.109285_b1410","series-title":"2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST)","first-page":"1","article-title":"An Automated Alert System for Monitoring the Hygiene in Restaurants Using Machine Vision","author":"Radhakrishnan","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1415","doi-asserted-by":"crossref","DOI":"10.1080\/21505594.2023.2274640","article-title":"A novel quorum sensing regulator LuxT contributes to the virulence of Vibrio cholerae","volume":"14","author":"Li","year":"2023","journal-title":"Virulence"},{"key":"10.1016\/j.bspc.2025.109285_b1420","article-title":"Kothandapani Sundar, \u201cQuorum Sensing Based Drug Screening Against Vibrio Cholerae,\u201d","volume":"1","author":"Mahima M","year":"2022","journal-title":"J. Microbes and Res."},{"key":"10.1016\/j.bspc.2025.109285_b1425","first-page":"1","article-title":"Vibrio cholerae virulence and its suppression through the quorum-sensing system","author":"Sajeevan","year":"2024","journal-title":"Crit. Rev. Microbiol."},{"key":"10.1016\/j.bspc.2025.109285_b1430","doi-asserted-by":"crossref","unstructured":"L. M. Walker, J. R. Haycocks, J. C. van Kessel, T. N. Dalia, A. B. Dalia, and D. C. Grainger, \u201cA simple mechanism for integration of quorum sensing and cAMP signalling in V. cholerae,\u201d BioRxiv, 2023.","DOI":"10.7554\/eLife.86699.1"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b1435","doi-asserted-by":"crossref","first-page":"20","DOI":"10.59067\/afjhms.v9i2.51","article-title":"A tool to monitor multi-sectoral response activities during outbreaks of Cholera in Sudan","volume":"9","author":"El Bushra","year":"2024","journal-title":"African J. Health and Medical Sciences (AFJHMS)"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1440","doi-asserted-by":"crossref","DOI":"10.1177\/14604582241275844","article-title":"AI-based epidemic and pandemic early warning systems: a systematic scoping review","volume":"30","author":"El Morr","year":"2024","journal-title":"Health Informatics J."},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b1445","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2023-013615","article-title":"How feasible or useful are timeliness metrics as a tool to optimise one Health outbreak responses?","volume":"9","author":"Fieldhouse","year":"2024","journal-title":"BMJ Glob. Health"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1450","doi-asserted-by":"crossref","first-page":"078","DOI":"10.53771\/ijlsra.2024.7.1.0062","article-title":"Artificial Intelligence in predictive analytics for epidemic outbreaks in rural populations","volume":"7","author":"Nwankwo","year":"2024","journal-title":"Int. J. Life Sci. Res. Archive"},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b1455","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.51594\/csitrj.v5i5.1118","article-title":"Environmental data in epidemic forecasting: Insights from predictive analytics","volume":"5","author":"Ebulue","year":"2024","journal-title":"Comput. Sci. IT Res. J."},{"key":"10.1016\/j.bspc.2025.109285_b1460","unstructured":"A. Isiaka et al., \u201cHarnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks,\u201d International Journal of Innovative Research and Development. 2024."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1465","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41540-023-00304-6","article-title":"Quantitative systems-based prediction of antimicrobial resistance evolution","volume":"9","author":"Charlebois","year":"2023","journal-title":"npj Syst. Biol. Appl."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1470","doi-asserted-by":"crossref","first-page":"e00179","DOI":"10.1128\/cmr.00179-21","article-title":"Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective","volume":"35","author":"Kim","year":"2022","journal-title":"Clin. Microbiol. Rev."},{"key":"10.1016\/j.bspc.2025.109285_b1475","doi-asserted-by":"crossref","unstructured":"V. Kolluru, Y. Nuthakki, S. Koganti, and A. N. Chintakunta, \u201cUse of predictive analytics in antimicrobial resistance: a review,\u201d Vinoth Kumar Kolluru et al, Cognizance Journal of Multidisciplinary Studies, vol. 4, no. 1, pp. 404-414, 2024.","DOI":"10.47760\/cognizance.2024.v04i01.020"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1480","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1186\/s12864-024-10214-4","article-title":"Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa","volume":"25","author":"Nsubuga","year":"2024","journal-title":"BMC Genomics"},{"key":"10.1016\/j.bspc.2025.109285_b1485","doi-asserted-by":"crossref","unstructured":"A. Roche-Lima et al., \u201cML-SyPred\u2013Machine Learning Synergy Predictor: A Tool to Predict Drug Combinations,\u201d 2022.","DOI":"10.21203\/rs.3.rs-1789485\/v1"},{"key":"10.1016\/j.bspc.2025.109285_b1490","doi-asserted-by":"crossref","unstructured":"C. Weis et al., \u201cDirect Antimicrobial Resistance Prediction from MALDI-TOF mass spectra profile in clinical isolates through Machine Learning,\u201d bioRxiv, vol. 1, pp. 1-35, 2020.","DOI":"10.1101\/2020.07.30.228411"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1495","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s44259-023-00015-2","article-title":"Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections","volume":"1","author":"Yang","year":"2023","journal-title":"Npj Antimicrobials and Resistance"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1500","doi-asserted-by":"crossref","DOI":"10.1080\/19476337.2024.2324024","article-title":"Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens","volume":"22","author":"Yun","year":"2024","journal-title":"CyTA-Journal of Food"},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b1505","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijantimicag.2024.107323","article-title":"Strategies for Quorum Sensing inhibition as a tool for controlling Pseudomonas aeruginosa infections","volume":"64","author":"Rodr\u00edguez-Urretavizcaya","year":"2024","journal-title":"Int. J. Antimicrob. Agents"},{"key":"10.1016\/j.bspc.2025.109285_b1510","doi-asserted-by":"crossref","DOI":"10.1016\/j.micres.2024.127915","article-title":"Revealing quorum-sensing networks in Pseudomonas aeruginosa infections through internal and external signals to prevent new resistance trends","author":"Guo","year":"2024","journal-title":"Microbiol. Res."},{"key":"10.1016\/j.bspc.2025.109285_b1515","unstructured":"A. S. Vivek Kumar, Deepmala Sharma, Vishnu Agarwal, Identification of novel potential anti-quorum sensing molecules against LasR of Pseudomonas aeruginosa using in silico approach. 1, 2024, pp. 177-183."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b1520","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0157334","article-title":"Thiophenone attenuates enteropathogenic Escherichia coli O103: H2 virulence by interfering with AI-2 signaling","volume":"11","author":"Wits\u00f8","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2025.109285_b1525","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1007\/s00253-020-10349-w","article-title":"Prevention of biofilm formation by quorum quenching","volume":"104","author":"Paluch","year":"2020","journal-title":"Appl. Microbiol. Biotechnol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1530","doi-asserted-by":"crossref","first-page":"e03796","DOI":"10.1128\/mbio.03796-21","article-title":"Genome informatics and machine learning-based identification of antimicrobial resistance-encoding features and virulence attributes in Escherichia coli genomes representing globally prevalent lineages, including high-risk clonal complexes","volume":"13","author":"Shaik","year":"2022","journal-title":"MBio"},{"issue":"12","key":"10.1016\/j.bspc.2025.109285_b1535","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1021\/acsinfecdis.0c00654","article-title":"Chemical control of quorum sensing in E. coli: identification of small molecule modulators of Sdia and mechanistic characterization of a covalent inhibitor","volume":"6","author":"Styles","year":"2020","journal-title":"ACS Infect. Dis."},{"key":"10.1016\/j.bspc.2025.109285_b1540","unstructured":"S. A. Princy, V. P. Krishna, V. Ravichandiran, and S. Rangarajan, \u201cE. coli quorum sensing: Appraisal towards curtailing pathogenesis,\u201d 2014."},{"issue":"31","key":"10.1016\/j.bspc.2025.109285_b1545","doi-asserted-by":"crossref","first-page":"9059","DOI":"10.1002\/ange.201602974","article-title":"Highly stable, amide\u2010bridged autoinducing peptide analogues that strongly inhibit the agrc quorum sensing receptor in Staphylococcus aureus","volume":"128","author":"Tal-Gan","year":"2016","journal-title":"Angew. Chem."},{"key":"10.1016\/j.bspc.2025.109285_b1550","doi-asserted-by":"crossref","unstructured":"N. Kumar, H. Gupta, N. Dhasmana, and Y. Singh, \u201cCombating Staphylococcal Infections Through Quorum Sensing Inhibitors,\u201d Biotechnological Applications of Quorum Sensing Inhibitors, pp. 309-325, 2018.","DOI":"10.1007\/978-981-10-9026-4_15"},{"key":"10.1016\/j.bspc.2025.109285_b1555","doi-asserted-by":"crossref","first-page":"441","DOI":"10.3389\/fcimb.2017.00441","article-title":"Modulating the global response regulator, LuxO of V. cholerae quorum sensing system using a pyrazine dicarboxylic acid derivative (PDCApy): an antivirulence approach","volume":"7","author":"Hema","year":"2017","journal-title":"Front. Cell. Infect. Microbiol."},{"key":"10.1016\/j.bspc.2025.109285_b1560","doi-asserted-by":"crossref","unstructured":"S. Agrebi and A. Larbi, \u201cUse of artificial intelligence in infectious diseases,\u201d. In Artificial intelligence in precision health: Elsevier, 2020, pp. 415-438.","DOI":"10.1016\/B978-0-12-817133-2.00018-5"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1565","doi-asserted-by":"crossref","DOI":"10.36877\/pmmb.a0000141","article-title":"Insights into quorum sensing (QS): QS-regulated biofilm and inhibitors","volume":"3","author":"Tan","year":"2020","journal-title":"Progress in Microbes and Molecular Biology"},{"key":"10.1016\/j.bspc.2025.109285_b1570","first-page":"129","article-title":"Interceptaci\u00f3n de se\u00f1ales de comunicaci\u00f3n bacteriana en bacterias aisladas del medio marino","volume":"29","author":"Bern\u00e1rdez","year":"2010","journal-title":"Revista Real Academia Galega De Ciencias"},{"key":"10.1016\/j.bspc.2025.109285_b1575","doi-asserted-by":"crossref","DOI":"10.1016\/j.micpath.2024.106609","article-title":"Attenuation of Las\/Rhl quorum sensing regulated virulence and biofilm formation in Pseudomonas aeruginosa PAO1 by Artocarpesin","volume":"189","author":"Mohan","year":"2024","journal-title":"Microb. Pathog."},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b1580","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijantimicag.2024.107323","article-title":"Strategies for quorum sensing inhibition as a tool for controlling Pseudomonas aeruginosa infections","volume":"64","author":"Rodr\u00edguez-Urretavizcaya","year":"2024","journal-title":"Int. J. Antimicrob. Agents"},{"issue":"12","key":"10.1016\/j.bspc.2025.109285_b1585","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1021\/acsinfecdis.0c00654","article-title":"Chemical control of quorum sensing in E.coli: identification of small molecule modulators of SdiA and mechanistic characterization of a covalent inhibitor","volume":"6","author":"Styles","year":"2020","journal-title":"ACS Infect. Dis."},{"issue":"31","key":"10.1016\/j.bspc.2025.109285_b1590","doi-asserted-by":"crossref","first-page":"8913","DOI":"10.1002\/anie.201602974","article-title":"Highly stable, amide-bridged autoinducing peptide analogues that strongly Inhibit the AgrC quorum sensing receptor in staphylococcus aureus","volume":"55","author":"Tal-Gan","year":"2016","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.bspc.2025.109285_b1595","series-title":"Biotechnological Applications of Quorum Sensing Inhibitors","first-page":"309","article-title":"Combating staphylococcal infections through quorum sensing inhibitors","author":"Kumar","year":"2018"},{"key":"10.1016\/j.bspc.2025.109285_b1600","doi-asserted-by":"crossref","unstructured":"K. Sundar, \u201cQuorum Sensing Based Drug Screening Against Vibrio Cholerae,\u201d Journal of Microbes and Research, vol. 1, pp. 01-05, 11\/28 2022, doi: 10.58489\/2836-2187\/001.","DOI":"10.58489\/2836-2187\/001"},{"key":"10.1016\/j.bspc.2025.109285_b1605","doi-asserted-by":"crossref","unstructured":"M. Hema, S. Vasudevan, P. Balamurugan, and S. Adline Princy, \u201cModulating the Global Response Regulator, LuxO of V. cholerae Quorum Sensing System Using a Pyrazine Dicarboxylic Acid Derivative (PDCApy): An Antivirulence Approach,\u201d (in English), Frontiers in Cellular and Infection Microbiology, Original Research vol. Volume 7 - 2017, 2017-October-12 2017, doi: 10.3389\/fcimb.2017.00441.","DOI":"10.3389\/fcimb.2017.00441"},{"key":"10.1016\/j.bspc.2025.109285_b1610","doi-asserted-by":"crossref","unstructured":"S. Agrebi and A. Larbi, \u201cChapter 18 - Use of artificial intelligence in infectious diseases,\u201d. In: Artificial Intelligence in Precision Health, D. Barh Ed.: Academic Press, 2020, pp. 415-438.","DOI":"10.1016\/B978-0-12-817133-2.00018-5"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1615","doi-asserted-by":"crossref","DOI":"10.36877\/pmmb.a0000141","article-title":"Insights into quorum sensing (QS): QS-regulated biofilm and inhibitors","volume":"3","author":"Tan","year":"2020","journal-title":"Progress In Microbes & Molecular Biology"},{"key":"10.1016\/j.bspc.2025.109285_b1620","doi-asserted-by":"crossref","unstructured":"R. Naderi and N. Mozayani, \u201cGene Regulatory Networks Full Observable Cbased on Batch Reinforcement Learning: An Improved Policy,\u201d. In: 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019: IEEE, pp. 2004-2009.","DOI":"10.1109\/IranianCEE.2019.8786638"},{"issue":"13","key":"10.1016\/j.bspc.2025.109285_b1625","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1089\/104303403767740795","article-title":"System for simultaneous tissue-specific and disease-specific regulation of therapeutic gene expression","volume":"14","author":"Chyung","year":"2003","journal-title":"Hum. Gene Ther."},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b1630","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1002\/bit.10731","article-title":"Artificial regulatory networks and cascades for discrete multilevel transgene control in mammalian cells","volume":"83","author":"Kramer","year":"2003","journal-title":"Biotechnol. Bioeng."},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b1635","doi-asserted-by":"crossref","first-page":"4447","DOI":"10.1073\/pnas.2001050117","article-title":"Immunoactivating the tumor microenvironment enhances immunotherapy as predicted by integrative computational model","volume":"117","author":"Popel","year":"2020","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b1640","doi-asserted-by":"crossref","first-page":"e471","DOI":"10.1093\/oncolo\/oyac036","article-title":"Artificial intelligence-based radiomics in the era of immuno-oncology","volume":"27","author":"Kang","year":"2022","journal-title":"Oncologist"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b1645","doi-asserted-by":"crossref","first-page":"123","DOI":"10.37349\/ei.2023.00093","article-title":"Chemokine-targeted nanoparticles: stimulation of the immune system in cancer immunotherapy","volume":"3","author":"Singh","year":"2023","journal-title":"Exploration of Immunology"},{"issue":"0506","key":"10.1016\/j.bspc.2025.109285_b1650","doi-asserted-by":"crossref","DOI":"10.4414\/smw.2015.14066","article-title":"Cancer immunology\u2013development of novel anti-cancer therapies","volume":"145","author":"Rothschild","year":"2015","journal-title":"Swiss Med. Wkly."},{"key":"10.1016\/j.bspc.2025.109285_b1655","doi-asserted-by":"crossref","unstructured":"M. D. Khichade, D. S. Shafi, P. S. Nilangekar, A. S. Gandhle, M. A . Gurav, and V. B. Phulsundar, \u201cImmunotherapy in Cancer: Biology Therapy,\u201d Journal of Biomedical and Pharmaceutical Research, vol. 11, no. 5, pp. 62-73, 11\/14 2022, doi: 10.32553\/jbpr.v11i5.937.","DOI":"10.32553\/jbpr.v11i5.937"},{"key":"10.1016\/j.bspc.2025.109285_b1660","doi-asserted-by":"crossref","unstructured":"W. You, A. Simalatsar, N. Widmer, and G. De Micheli, \u201cA drug administration decision support system,\u201d. In: 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2012: IEEE, pp. 122-129.","DOI":"10.1109\/BIBMW.2012.6470292"},{"key":"10.1016\/j.bspc.2025.109285_b1665","doi-asserted-by":"crossref","unstructured":"D. Paolino, P. Sinha, M. Fresta, and M. Ferrari, \u201cDrug delivery systems,\u201d Encyclopedia of medical devices and instrumentation, 2006.","DOI":"10.1002\/0471732877.emd274"},{"key":"10.1016\/j.bspc.2025.109285_b1670","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1007\/s11164-013-1338-2","article-title":"Advances and new technologies applied in controlled drug delivery system","volume":"41","author":"Bassyouni","year":"2015","journal-title":"Res. Chem. Intermed."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b1675","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/35101056","article-title":"Pharmacogenetics and cancer therapy","volume":"1","author":"Relling","year":"2001","journal-title":"Nat. Rev. Cancer"},{"key":"10.1016\/j.bspc.2025.109285_b1680","doi-asserted-by":"crossref","first-page":"S11","DOI":"10.1016\/j.metabol.2012.08.016","article-title":"Personalized oncology: recent advances and future challenges","volume":"62","author":"Kalia","year":"2013","journal-title":"Metabolism"},{"key":"10.1016\/j.bspc.2025.109285_b1685","article-title":"AI-Based solutions for current challenges in regenerative medicine","volume":"984","author":"Sarabi","year":"2024","journal-title":"Eur. J. Pharmacol."},{"key":"10.1016\/j.bspc.2025.109285_b1690","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.73870","article-title":"Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics","volume":"11","author":"Baranwal","year":"2022","journal-title":"Elife"},{"key":"10.1016\/j.bspc.2025.109285_b1695","unstructured":"L. N. Merk, A. S. Shur, A. Pandey, R. M. Murray, and L. N. Green, \u201cEngineering logical inflammation sensing circuit for gut modulation,\u201d bioRxiv, vol. 10, no. 2020.11, p. 10.377085, 2020."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1700","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1080\/1040841X.2023.2190392","article-title":"Engineered probiotics as live biotherapeutics for diagnosis and treatment of human diseases","volume":"50","author":"Meng","year":"2024","journal-title":"Crit. Rev. Microbiol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1705","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1146\/annurev-genom-110122-084756","article-title":"Federated analysis for privacy-preserving data sharing: a technical and legal primer","volume":"24","author":"Casaletto","year":"2023","journal-title":"Annu. Rev. Genomics Hum. Genet."},{"issue":"1150","key":"10.1016\/j.bspc.2025.109285_b1710","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20220934","article-title":"AI and machine learning ethics, law, diversity, and global impact","volume":"96","author":"Drabiak","year":"2023","journal-title":"Br. J. Radiol."},{"key":"10.1016\/j.bspc.2025.109285_b1715","series-title":"Artificial Intelligence in Science Challenges, Opportunities and the Future of Research: Challenges, Opportunities and the Future of Research","first-page":"170","article-title":"Applying AI to real-world health-care settings and the life sciences: tackling data privacy, security and policy challenges with federated learning","author":"Galtier","year":"2023"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1720","doi-asserted-by":"crossref","first-page":"707","DOI":"10.30574\/wjarr.2024.23.3.2660","article-title":"Machine learning models for personalised healthcare on marketable generative-AI with ethical implications","volume":"23","author":"Gulia","year":"2024","journal-title":"World J. Adv. Res. Rev"},{"key":"10.1016\/j.bspc.2025.109285_b1725","doi-asserted-by":"crossref","DOI":"10.3389\/fbinf.2023.1191961","article-title":"Omics data integration in computational biology viewed through the prism of machine learning paradigms","volume":"3","author":"Fouch\u00e9","year":"2023","journal-title":"Frontiers in Bioinformatics"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1730","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13040-024-00391-z","article-title":"Deep learning-based approaches for multi-omics data integration and analysis","volume":"17","author":"Ballard","year":"2024","journal-title":"Biodata Min."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1735","first-page":"1","article-title":"Assessing the performance of machine learning models for default prediction under missing data and class imbalance: a simulation study","volume":"40","author":"Dube","year":"2024","journal-title":"ORiON"},{"key":"10.1016\/j.bspc.2025.109285_b1740","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107803","article-title":"Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: a comparative of solutions in a COVID-19 mortality case study","volume":"242","author":"Ferri","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.bspc.2025.109285_b1745","doi-asserted-by":"crossref","unstructured":"J. Shi, A. Hubbard, N. Fong, and R. Pirracchio, \u201cImplicit bias in Critical Care Data: Factors affecting sampling frequencies and missingness patterns of clinical and biological variables in ICU Patients,\u201d medRxiv, p. 2024.06. 09.24308661, 2024.","DOI":"10.1101\/2024.06.09.24308661"},{"key":"10.1016\/j.bspc.2025.109285_b1750","doi-asserted-by":"crossref","unstructured":"A. Emili, \u201cState of the art in data integration and network biology,\u201d Authorea Preprints, 2024.","DOI":"10.22541\/au.170664293.30945590\/v1"},{"key":"10.1016\/j.bspc.2025.109285_b1755","article-title":"Amalur: Data integration meets machine learning","author":"Li","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1760","doi-asserted-by":"crossref","first-page":"46","DOI":"10.48185\/jitc.v4i1.725","article-title":"Quality challenges in deep learning data collection in perspective of artificial intelligence","volume":"4","author":"Vidhya","year":"2023","journal-title":"J. Information Technol. Computing"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b1765","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.52783\/tjjpt.v44.i3.488","article-title":"Integrative analysis of multi-omics data with deep learning: challenges and opportunities in bioinformatics","volume":"44","author":"Gonesh Chandra Saha","year":"2023","journal-title":"Tuijin Jishu\/J. Propulsion Technol."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b1770","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad351","article-title":"Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes","volume":"24","author":"Adams","year":"2023","journal-title":"Brief. Bioinform."},{"issue":"01","key":"10.1016\/j.bspc.2025.109285_b1775","doi-asserted-by":"crossref","first-page":"9434","DOI":"10.1609\/aaai.v33i01.33019434","article-title":"A fast machine learning workflow for rapid phenotype prediction from whole shotgun metagenomes","volume":"33","author":"Carrieri","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.bspc.2025.109285_b1780","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.119814","article-title":"QSP: an open sequence database for quorum sensing related gene analysis with an automatic annotation pipeline","volume":"235","author":"Dai","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.bspc.2025.109285_b1785","series-title":"Real-Time Modelling and Processing for Communication Systems: Applications and Practices","first-page":"1","article-title":"Real-Time Modelling and Processing","author":"Dghais","year":"2018"},{"key":"10.1016\/j.bspc.2025.109285_b1790","first-page":"123","article-title":"MRQPMS, design of a map reduce bioinspired model for solving quorum planted motif search for High-speed deployments","author":"Durge","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.bspc.2025.109285_b1795","doi-asserted-by":"crossref","unstructured":"A. Khodabakhsh, T. P. Loka, S. Boutin, D. Nurjadi, and B. Y. Renard, \u201cPredicting decision-making time for diagnosis over ngs cycles: An interpretable machine learning approach,\u201d bioRxiv, p. 2023.03. 07.530760, 2023.","DOI":"10.1101\/2023.03.07.530760"},{"key":"10.1016\/j.bspc.2025.109285_b1800","doi-asserted-by":"crossref","unstructured":"R. Nussinov and J. A. Papin, \u201cHow can computation advance microbiome research?,\u201d vol. 13, ed: Public Library of Science San Francisco, CA USA, 2017, p. e1005547.","DOI":"10.1371\/journal.pcbi.1005547"},{"key":"10.1016\/j.bspc.2025.109285_b1805","unstructured":"A. Karuvally and J. E. B. Moss, \u201cModel Complexity of Program Phases,\u201d arXiv preprint arXiv:2310.03865, 2023."},{"key":"10.1016\/j.bspc.2025.109285_b1810","doi-asserted-by":"crossref","DOI":"10.1016\/j.copbio.2022.102704","article-title":"Fighting fire with fire: deploying complexity in computational modeling to effectively characterize complex biological systems","volume":"75","author":"Prybutok","year":"2022","journal-title":"Curr. Opin. Biotechnol."},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b1815","doi-asserted-by":"crossref","first-page":"2260","DOI":"10.1021\/acssynbio.4c00116","article-title":"Modeling microbial communities: perspective and challenges","volume":"13","author":"Raajaraam","year":"2024","journal-title":"ACS Synth. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b1820","doi-asserted-by":"crossref","first-page":"38","DOI":"10.34229\/2707-451X.22.2.4","article-title":"Models of computer calculations","volume":"2","author":"Zadiraka","year":"2022","journal-title":"Cybernetics and Comput. Technol."},{"key":"10.1016\/j.bspc.2025.109285_b1825","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2024.3402061","article-title":"A multiple controlled toffoli driven adaptive quantum neural network model for dynamic workload prediction in cloud environments","author":"Gupta","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bspc.2025.109285_b1830","series-title":"International Conference on Distributed Computer and Communication Networks","first-page":"68","article-title":"Distributed System for Scientific and Engineering computations with Problem Containerization and Prioritization","author":"Sokolov","year":"2023"},{"key":"10.1016\/j.bspc.2025.109285_b1835","series-title":"2024 5th International Conference on Recent Trends in Computer Science and Technology","first-page":"695","article-title":"Potential in quantum machine learning for real-world problems","author":"Tiwari","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b1840","first-page":"246","article-title":"Optimizing real-time systems with parallel computing: techniques and challenges","volume":"3","author":"Narotam Dass","year":"2024","journal-title":"Futuristic Trends in Information Technology IIP Series"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1845","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s40537-023-00727-2","article-title":"A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications","volume":"10","author":"Alzubaidi","year":"2023","journal-title":"J. Big Data"},{"key":"10.1016\/j.bspc.2025.109285_b1850","doi-asserted-by":"crossref","unstructured":"C. Chai et al., \u201cMitigating Data Scarcity in Supervised Machine Learning Through Reinforcement Learning Guided Data Generation,\u201d. In: 2024 IEEE 40th International Conference on Data Engineering (ICDE), 2024: IEEE, pp. 3613-3626.","DOI":"10.1109\/ICDE60146.2024.00278"},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b1855","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1021\/jasms.4c00180","article-title":"Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics","volume":"35","author":"Dens","year":"2024","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"10.1016\/j.bspc.2025.109285_b1860","unstructured":"Y. Li, Y. Kim, D. Lee, and P. Panda, \u201cStableQ: Enhancing Data-Scarce Quantization with Text-to-Image Data,\u201d arXiv preprint arXiv:2312.05272, 2023."},{"key":"10.1016\/j.bspc.2025.109285_b1865","unstructured":"O. Niel, \u201cA novel algorithm can generate data to train machine learning models in conditions of extreme scarcity of real world data,\u201d arXiv preprint arXiv:2305.00987, 2023."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1870","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1038\/s41598-024-59958-9","article-title":"Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance","volume":"14","author":"Hakami","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.bspc.2025.109285_b1875","series-title":"In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24)","article-title":"Machine unlearning: challenges in data quality and access","author":"Xu","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b1880","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2023.1225409","article-title":"Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning","volume":"14","author":"Xu","year":"2023","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.bspc.2025.109285_b1885","doi-asserted-by":"crossref","unstructured":"J. Chen, Y. Zhang, B. Wang, W. X. Zhao, J.-R. Wen, and W. Chen, \u201cUnveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models,\u201d arXiv preprint arXiv:2406.12397, 2024.","DOI":"10.18653\/v1\/2024.findings-emnlp.873"},{"key":"10.1016\/j.bspc.2025.109285_b1890","doi-asserted-by":"crossref","unstructured":"S. Gandhi, R. Gala, V. Viswanathan, T. Wu, and G. Neubig, \u201cBetter Synthetic Data by Retrieving and Transforming Existing Datasets,\u201d arXiv preprint arXiv:2404.14361, 2024.","DOI":"10.18653\/v1\/2024.findings-acl.385"},{"issue":"14","key":"10.1016\/j.bspc.2025.109285_b1895","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.3390\/app14145975","article-title":"Challenges of using synthetic data generation methods for tabular microdata","volume":"14","author":"Miletic","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2025.109285_b1900","doi-asserted-by":"crossref","unstructured":"H. B. Braiek and F. Khomh, \u201cMachine Learning Robustness: A Primer,\u201d arXiv preprint arXiv:2404.00897, 2024.","DOI":"10.1016\/B978-0-44-323761-4.00012-2"},{"key":"10.1016\/j.bspc.2025.109285_b1905","doi-asserted-by":"crossref","unstructured":"F. Guillaume, \u201cGenetic and environmental robustness are distinct yet correlated evolvable traits in a gene network,\u201d Peer Community In Evolutionary Biology, p. 100138, 2022.","DOI":"10.24072\/pci.evolbiol.100138"},{"issue":"8","key":"10.1016\/j.bspc.2025.109285_b1910","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.tibtech.2022.01.004","article-title":"Robustness: linking strain design to viable bioprocesses","volume":"40","author":"Olsson","year":"2022","journal-title":"Trends Biotechnol."},{"key":"10.1016\/j.bspc.2025.109285_b1915","series-title":"Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering","first-page":"1","article-title":"A behavioral notion of robustness for software systems","author":"Zhang","year":"2020"},{"key":"10.1016\/j.bspc.2025.109285_b1920","doi-asserted-by":"crossref","unstructured":"B. W. Herken, G. T. Wong, T. M. Norman, and L. A. Gilbert, \u201cEnvironmental challenge rewires functional connections among human genes,\u201d bioRxiv, 2023.","DOI":"10.1101\/2023.08.09.552346"},{"key":"10.1016\/j.bspc.2025.109285_b1925","doi-asserted-by":"crossref","unstructured":"K. Kochanowski, T. Sander, H. Link, J. Chang, S. Altschuler, and L. Wu, \u201cDissecting the impact of metabolic environment on three common cancer cell phenotypes,\u201d bioRxiv, p. 2020.06. 23.167437, 2020.","DOI":"10.1101\/2020.06.23.167437"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1930","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41540-020-00155-5","article-title":"Environmental flexibility does not explain metabolic robustness","volume":"6","author":"Libiseller-Egger","year":"2020","journal-title":"npj Syst. Biol. Appl."},{"key":"10.1016\/j.bspc.2025.109285_b1935","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/978-3-030-52017-5_20","article-title":"Robust computing for machine learning-based systems","author":"Hanif","year":"2021","journal-title":"Dependable Embedded Systems"},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b1940","doi-asserted-by":"crossref","first-page":"4176","DOI":"10.1109\/TSMC.2023.3241621","article-title":"Enhancing the robustness of networks against multiple damage models using a multifactorial evolutionary algorithm","volume":"53","author":"Wang","year":"2023","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"10.1016\/j.bspc.2025.109285_b1945","article-title":"Improve robustness of machine learning via efficient optimization and conformal prediction","author":"Yan","year":"2024","journal-title":"AI Mag."},{"key":"10.1016\/j.bspc.2025.109285_b1950","doi-asserted-by":"crossref","unstructured":"X. Zhou, M. Kouzel, and H. Alemzadeh, \u201cRobustness testing of data and knowledge driven anomaly detection in cyber-physical systems,\u201d. In: 2022 52nd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2022: IEEE, pp. 44-51.","DOI":"10.1109\/DSN-W54100.2022.00017"},{"key":"10.1016\/j.bspc.2025.109285_b1955","doi-asserted-by":"crossref","unstructured":"M. De Domenico et al., \u201cChallenges and opportunities for digital twins in precision medicine: a complex systems perspective,\u201d arXiv preprint arXiv:2405.09649, 2024.","DOI":"10.1038\/s41746-024-01402-3"},{"key":"10.1016\/j.bspc.2025.109285_b1960","doi-asserted-by":"crossref","DOI":"10.3389\/fsysb.2024.1380685","article-title":"Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: its pAIns and gAIns","volume":"4","author":"Folguera-Blasco","year":"2024","journal-title":"Front. Syst. Biol."},{"key":"10.1016\/j.bspc.2025.109285_b1965","doi-asserted-by":"crossref","unstructured":"K. M. Habibullah, J. G. Diaz, G. Gay, and J. Horkoff, \u201cScoping of non-functional requirements for machine learning systems,\u201d. In: 2024 IEEE 32nd International Requirements Engineering Conference (RE), 2024: IEEE, pp. 496-497.","DOI":"10.1109\/RE59067.2024.00061"},{"key":"10.1016\/j.bspc.2025.109285_b1970","doi-asserted-by":"crossref","DOI":"10.1111\/eci.14222","article-title":"Progress and challenges in the symbiosis of AI with science and medicine","author":"Lin","year":"2024","journal-title":"Eur. J. Clin. Invest."},{"key":"10.1016\/j.bspc.2025.109285_b1975","article-title":"Applications of large language models in clinical practice: path challenges, and future perspectives","author":"Liu","year":"2024","journal-title":"OSF Preprints"},{"key":"10.1016\/j.bspc.2025.109285_b1980","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1002\/9781394227990.ch2","article-title":"Challenges in building predictive models","author":"Nayak","year":"2024","journal-title":"Intelligent Techniques for Predictive Data Analytics"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b1985","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13000-024-01464-7","article-title":"Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology\u2013a recent scoping review","volume":"19","author":"Ullah","year":"2024","journal-title":"Diagn. Pathol."},{"key":"10.1016\/j.bspc.2025.109285_b1990","doi-asserted-by":"crossref","unstructured":"A. Z. A. Farazin, \u201cAdvancements and Challenges in the Application of Machine Learning for Biomedical Diagnostics and Disease Prediction,\u201d SciBase Journals, vol. 2, no. 3, 2024, doi: https:\/\/www.doi.org\/10.52768\/casereports\/1026.","DOI":"10.52768\/casereports\/1026"},{"key":"10.1016\/j.bspc.2025.109285_b1995","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"10.1016\/j.bspc.2025.109285_b2000","article-title":"forecasting: principles and practice","author":"Hyndman","year":"2018","journal-title":"Otexts"},{"key":"10.1016\/j.bspc.2025.109285_b2005","doi-asserted-by":"crossref","unstructured":"G. S. Collins et al., \u201cEvaluation of clinical prediction models (part 1): from development to external validation,\u201d (in eng), Bmj, vol. 384, p. e074819, Jan 8 2024, doi: 10.1136\/bmj-2023-074819.","DOI":"10.1136\/bmj-2023-074819"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b2010","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another look at measures of forecast accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"issue":"9","key":"10.1016\/j.bspc.2025.109285_b2015","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1037-7","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension","volume":"26","author":"Cruz Rivera","year":"2020","journal-title":"Nat. Med."},{"key":"10.1016\/j.bspc.2025.109285_b2020","unstructured":"WHO. \u201cWHO releases AI ethics and governance guidance for large multi-modal models.\u201d https:\/\/www.who.int\/news\/item\/18-01-2024-who-releases-ai-ethics-and-governance-guidance-for-large-multi-modal-models (accessed 24 Sep, 2025)."},{"key":"10.1016\/j.bspc.2025.109285_b2025","series-title":"EcoDesign and Sustainability II: Social Perspectives and Sustainability Assessment","first-page":"385","article-title":"Environmental and Economic Impacts of Biofouling on Marine and Coastal Heat Exchangers","author":"Mathew","year":"2021"},{"key":"10.1016\/j.bspc.2025.109285_b2030","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2023.1244595","article-title":"Do biosurfactants as anti-biofilm agents have a future in industrial water systems?","volume":"11","author":"Jimoh","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"key":"10.1016\/j.bspc.2025.109285_b2035","unstructured":"WHO. \u201cAntimicrobial resistance.\u201d https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/antimicrobial-resistance (accessed 25 Sep, 2025)."},{"key":"10.1016\/j.bspc.2025.109285_b2040","doi-asserted-by":"crossref","DOI":"10.1016\/j.fusengdes.2020.112022","article-title":"Cost-benefit analysis of condition monitoring on DEMO remote maintenance system","volume":"160","author":"Petkov","year":"2020","journal-title":"Fusion Eng. Des."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b2045","doi-asserted-by":"crossref","DOI":"10.1088\/1367-2630\/ac5b56","article-title":"Performance analysis of a hybrid agent for quantum-accessible reinforcement learning","volume":"24","author":"Hamann","year":"2022","journal-title":"New J. Phys."},{"key":"10.1016\/j.bspc.2025.109285_b2050","unstructured":"B. R. W. Joshua R. Bertram and R. A. Mclean Angus L, \u201cSafe and secure practical autonomy,\u201d 2020."},{"key":"10.1016\/j.bspc.2025.109285_b2055","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1109\/LCSYS.2021.3094764","article-title":"Learning Q-function approximations for hybrid control problems","volume":"6","author":"Menta","year":"2021","journal-title":"IEEE Control Syst. Lett."},{"key":"10.1016\/j.bspc.2025.109285_b2060","series-title":"International Conference on Communication and Network Technology","first-page":"159","article-title":"Quantum-Secure Autonomous factories: Hybrid TLS 1.3 for Inter-and Intra-plant Communication","author":"Rohde","year":"2022"},{"key":"10.1016\/j.bspc.2025.109285_b2065","doi-asserted-by":"crossref","unstructured":"H. Wang, L. Stobaugh, and W. Stobaugh, \u201cHybrid Quanvolutional Regression Neural Network: Quantum Advantage for Near-term Autonomous Machine,\u201d Authorea Preprints, 2024.","DOI":"10.36227\/techrxiv.170250776.61395845\/v3"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2070","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MCI.2022.3222057","article-title":"Controlling sequential hybrid evolutionary algorithm by Q-learning [research frontier][research frontier]","volume":"18","author":"Zhang","year":"2023","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b2075","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevApplied.17.024069","article-title":"Toward robust autotuning of noisy quantum dot devices","volume":"17","author":"Ziegler","year":"2022","journal-title":"Phys. Rev. Appl"},{"key":"10.1016\/j.bspc.2025.109285_b2080","doi-asserted-by":"crossref","unstructured":"G. Bakirtzis, M. Chiou, and A. Theodorou, \u201cNegotiating Control: Neurosymbolic Variable Autonomy,\u201d arXiv preprint arXiv:2407.16254, 2024.","DOI":"10.3233\/FAIA240432"},{"key":"10.1016\/j.bspc.2025.109285_b2085","doi-asserted-by":"crossref","unstructured":"V. Da Poian, E. Lyness, R. Danell, B. Theiling, and W. Brinckerhoff, \u201cScience Autonomy and Planetary Missions: ML and Data Science Applied to the ExoMars Mission,\u201d. In: 2023 IEEE Aerospace Conference, 2023: IEEE, pp. 1-7.","DOI":"10.1109\/AERO55745.2023.10115830"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2090","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/MTS.2023.3241315","article-title":"Public policy challenges, regulations, oversight, technical, and ethical considerations for autonomous systems: a survey","volume":"42","author":"Fard","year":"2023","journal-title":"IEEE Technol. Soc. Mag."},{"key":"10.1016\/j.bspc.2025.109285_b2095","unstructured":"M. Hamad and S. Steinhorst, \u201cSecurity Challenges in Autonomous Systems Design,\u201d arXiv preprint arXiv:2312.00018, 2023."},{"key":"10.1016\/j.bspc.2025.109285_b2100","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1613\/jair.1.13581","article-title":"Avoiding negative side effects of autonomous systems in the open world","volume":"74","author":"Saisubramanian","year":"2022","journal-title":"J. Artif. Intell. Res."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b2105","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1007\/s10270-021-00956-0","article-title":"Decision-making under uncertainty: be aware of your priorities","volume":"21","author":"Samin","year":"2022","journal-title":"Softw. Syst. Model."},{"key":"10.1016\/j.bspc.2025.109285_b2110","doi-asserted-by":"crossref","unstructured":"S. Straube, N. Hoyer, N. Will, and F. Kirchner, \u201cThe Challenge of Autonomy: What We Can Learn from Research on Robots Designed for Harsh Environments,\u201d. In: Robots in Care and Everyday Life: Future, Ethics, Social Acceptance: Springer International Publishing Cham, 2022, pp. 57-80.","DOI":"10.1007\/978-3-031-11447-2_4"},{"issue":"10","key":"10.1016\/j.bspc.2025.109285_b2115","doi-asserted-by":"crossref","DOI":"10.1002\/bies.202300015","article-title":"Trade\u2010offs between the instantaneous growth rate and long\u2010term fitness: consequences for microbial physiology and predictive computational models","volume":"45","author":"Bruggeman","year":"2023","journal-title":"Bioessays"},{"key":"10.1016\/j.bspc.2025.109285_b2120","doi-asserted-by":"crossref","unstructured":"M. Kapasiawala and R. M. Murray, \u201cMetabolic perturbations to an <em>E. coli<\/em>-based cell-free system reveal a trade-off between transcription and translation,\u201d bioRxiv, p. 2023.03.22.533877, 2024, doi: 10.1101\/2023.03.22.533877.","DOI":"10.1101\/2023.03.22.533877"},{"key":"10.1016\/j.bspc.2025.109285_b2125","unstructured":"J. M. Pittman, \u201cA Measure for Level of Autonomy Based on Observable System Behavior,\u201d arXiv preprint arXiv:2407.14975, 2024."},{"key":"10.1016\/j.bspc.2025.109285_b2130","doi-asserted-by":"crossref","unstructured":"E. E. Sparks and A. Rasmussen, \u201cTrade\u2010offs in plant responses to the environment,\u201d vol. 46, ed: Wiley Online Library, 2023, pp. 2943-2945.","DOI":"10.1111\/pce.14689"},{"key":"10.1016\/j.bspc.2025.109285_b2135","doi-asserted-by":"crossref","unstructured":"H. Abdelkader, M. Abdelrazek, J.-G. Schneider, P. Rani, and R. Vasa, \u201cRobustness Attributes to Safeguard Machine Learning Models in Production,\u201d. In: 2023 IEEE Engineering Informatics, 2023: IEEE, pp. 1-9.","DOI":"10.1109\/IEEECONF58110.2023.10520555"},{"key":"10.1016\/j.bspc.2025.109285_b2140","unstructured":"R. Y. Pang and K. Reinecke, \u201cAnticipating Unintended Consequences of Technology Using Insights from Creativity Support Tools,\u201d arXiv preprint arXiv:2304.05687, 2023."},{"key":"10.1016\/j.bspc.2025.109285_b2145","doi-asserted-by":"crossref","unstructured":"J. Shen, Z. Han, and W. Wang, \u201cRobustness of Modified Control Barrier Functions subject to External Disturbances,\u201d. In: 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA), 2023: IEEE, pp. 34-39.","DOI":"10.1109\/ICIEA58696.2023.10241419"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b2150","doi-asserted-by":"crossref","first-page":"923","DOI":"10.21275\/SR24409085438","article-title":"Robustness testing for AI\/ML models: strategies for identifying and mitigating vulnerabilities","volume":"13","author":"Praveen Kumar","year":"2024","journal-title":"Int. J. Sci. Res. (IJSR)"},{"key":"10.1016\/j.bspc.2025.109285_b2155","doi-asserted-by":"crossref","unstructured":"R. Clay-Williams, \u201cComplex systems and unintended consequences,\u201d in Implementation Science. Routledge, 2022.","DOI":"10.4324\/9781003109945-59"},{"key":"10.1016\/j.bspc.2025.109285_b2160","doi-asserted-by":"crossref","unstructured":"R. S. Ferreira, \u201cTowards safety monitoring of ML-based perception tasks of autonomous systems,\u201d. In: 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 2020: IEEE, pp. 135-138.","DOI":"10.1109\/ISSREW51248.2020.00052"},{"key":"10.1016\/j.bspc.2025.109285_b2165","doi-asserted-by":"crossref","unstructured":"J. Uddin, M. Ahad, and A. H. Kafi, \u201cWireless event-based kill-switch for safe and autonomous UAS operation,\u201d. In: 2023 International Conference on Electronics, Information, and Communication (ICEIC), 2023: IEEE, pp. 1-4.","DOI":"10.1109\/ICEIC57457.2023.10049917"},{"key":"10.1016\/j.bspc.2025.109285_b2170","doi-asserted-by":"crossref","unstructured":"M. Weiss and P. Tonella, \u201cFail-safe execution of deep learning based systems through uncertainty monitoring,\u201d. In: 2021 14th IEEE conference on software testing, verification and validation (ICST), 2021: IEEE, pp. 24-35.","DOI":"10.1109\/ICST49551.2021.00015"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2175","doi-asserted-by":"crossref","first-page":"52","DOI":"10.47941\/ijce.1714","article-title":"Security in Machine Learning (ML) workflows","volume":"5","author":"Dinesh Reddy Chittibala","year":"2024","journal-title":"Int. J. Computing and Eng."},{"key":"10.1016\/j.bspc.2025.109285_b2180","doi-asserted-by":"crossref","unstructured":"A. Causevic, A. V. Papadopoulos, and M. Sirjani, \u201cTowards a framework for safe and secure adaptive collaborative systems,\u201d. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), 2019, vol. 2: IEEE, pp. 165-170.","DOI":"10.1109\/COMPSAC.2019.10201"},{"key":"10.1016\/j.bspc.2025.109285_b2185","first-page":"7077","article-title":"On Adaptivity and Safety in Sequential Decision making","author":"Chaudhary","year":"2023","journal-title":"IJCAI"},{"issue":"19","key":"10.1016\/j.bspc.2025.109285_b2190","doi-asserted-by":"crossref","first-page":"20975","DOI":"10.1609\/aaai.v38i19.30088","article-title":"Constrained meta-reinforcement learning for adaptable safety guarantee with differentiable convex programming","volume":"38","author":"Cho","year":"2024","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.1016\/j.bspc.2025.109285_b2195","doi-asserted-by":"crossref","unstructured":"C. Dubslaff, K. Ding, A. Morozov, C. Baier, and K. Janschek, \u201cBreaking the limits of redundancy systems analysis,\u201d arXiv preprint arXiv:1912.05364, 2019.","DOI":"10.3850\/978-981-11-2724-3_0618-cd"},{"key":"10.1016\/j.bspc.2025.109285_b2200","doi-asserted-by":"crossref","unstructured":"E. Henkel, N. Hauff, L. Funk, V. Langenfeld, and A. Podelski, \u201cScalable Redundancy Detection for Real-Time Requirements,\u201d. In: 2024 IEEE 32nd International Requirements Engineering Conference (RE), 2024: IEEE, pp. 193-204.","DOI":"10.1109\/RE59067.2024.00027"},{"key":"10.1016\/j.bspc.2025.109285_b2205","doi-asserted-by":"crossref","unstructured":"A. Kajmakovic, R. Zupanc, S. Mayer, N. Kajtazovic, M. Hoeffernig, and H. Vogl, \u201cPredictive fail-safe improving the safety of industrial environments through model-based analytics on hidden data sources,\u201d .In: 2018 IEEE 13th International Symposium on Industrial Embedded Systems (SIES), 2018: IEEE, pp. 1-4.","DOI":"10.1109\/SIES.2018.8442104"},{"key":"10.1016\/j.bspc.2025.109285_b2210","doi-asserted-by":"crossref","unstructured":"S. Kulkarni, A. Marda, and K. Vaidhyanathan, \u201cTowards self-adaptive machine learning-enabled systems through QoS-aware model switching,\u201d .In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE), 2023: IEEE, pp. 1721-1725.","DOI":"10.1109\/ASE56229.2023.00172"},{"key":"10.1016\/j.bspc.2025.109285_b2215","article-title":"Safety-based prediction apparatus, system and method","author":"Ozer","year":"2022","journal-title":"Ed: Google Patents"},{"key":"10.1016\/j.bspc.2025.109285_b2220","series-title":"In Proceedings of the 26th Asia and South Pacific Design Automation Conference","first-page":"753","article-title":"Safety-assured design and adaptation of learning-enabled autonomous systems","author":"Zhu","year":"2021"},{"key":"10.1016\/j.bspc.2025.109285_b2225","unstructured":"M. E. Abdelkhalak El Hami, \u201cModeling of Systems with Redundancy,\u201d in Reliability\u2010based Modeling of System Performance, vol. 19. Wiley Online Library, 2023, ch. 4."},{"key":"10.1016\/j.bspc.2025.109285_b2230","doi-asserted-by":"crossref","DOI":"10.1146\/annurev-biodatasci-102623-104553","article-title":"Biomedical data science, artificial intelligence, and ethics: navigating challenges in the face of explosive growth","volume":"7","author":"Federico","year":"2024","journal-title":"Annu. Rev. Biomed. Data Sci."},{"key":"10.1016\/j.bspc.2025.109285_b2235","doi-asserted-by":"crossref","unstructured":"S. G. Johnson, G. Simon, and C. Aliferis, \u201cRegulatory Aspects and Ethical Legal Societal Implications (ELSI),\u201d Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls, pp. 659-692, 2024.","DOI":"10.1007\/978-3-031-39355-6_16"},{"issue":"31","key":"10.1016\/j.bspc.2025.109285_b2240","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2310458121","article-title":"Ethics and responsibility in biohybrid robotics research","volume":"121","author":"Mestre","year":"2024","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.bspc.2025.109285_b2245","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2023.1292029","article-title":"Safety risks and ethical governance of biomedical applications of synthetic biology","volume":"11","author":"Ou","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2250","doi-asserted-by":"crossref","first-page":"149","DOI":"10.47203\/IJCH.2024.v36i01.024","article-title":"Navigating the ethical landscape: implementing machine learning in smart healthcare informatics","volume":"36","author":"Sharma","year":"2024","journal-title":"Indian J. Community Health"},{"key":"10.1016\/j.bspc.2025.109285_b2255","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2024.1359768","article-title":"The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks","volume":"12","author":"Undheim","year":"2024","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b2260","doi-asserted-by":"crossref","first-page":"96","DOI":"10.31857\/S0236200724030065","article-title":"Ethical risks of artificial intelligence and prospects for joint decision-making in medicine","volume":"35","author":"Kochetova","year":"2024","journal-title":"\u010celovek"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b2265","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1177\/00243639231162431","article-title":"\u201cJust the Facts Ma\u2019am\u201d: moral and ethical considerations for artificial intelligence in medicine and its potential to impact patient autonomy and hope","volume":"90","author":"Love","year":"2023","journal-title":"Linacre Q."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2270","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1038\/s41746-023-00929-1","article-title":"Autonomous AI systems in the face of liability, regulations and costs","volume":"6","author":"Saenz","year":"2023","journal-title":"npj Digital Med."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b2275","doi-asserted-by":"crossref","first-page":"150","DOI":"10.4103\/singaporemedj.SMJ-2023-279","article-title":"Ethics of artificial intelligence in medicine","volume":"65","author":"Savulescu","year":"2024","journal-title":"Singapore Med. J."},{"key":"10.1016\/j.bspc.2025.109285_b2280","series-title":"In Proceedings of the First International Symposium on Trustworthy Autonomous Systems","first-page":"1","article-title":"Ethics of trust\/worthiness in autonomous systems: a scoping review","author":"Smith","year":"2023"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b2285","doi-asserted-by":"crossref","first-page":"483","DOI":"10.55730\/1300-0144.5814","article-title":"How to mitigate the risks of deployment of artificial intelligence in medicine?","volume":"54","author":"Uygun \u0130likhan","year":"2024","journal-title":"Turkish J. Medical Sci."},{"issue":"5","key":"10.1016\/j.bspc.2025.109285_b2290","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/15265161.2023.2191052","article-title":"Is the algorithm good in a bad world, or has it learned to be bad? The ethical challenges of \u201clocked\u201d versus \u201ccontinuously learning\u201d and \u201cautonomous\u201d versus \u201cassistive\u201d ai tools in healthcare","volume":"23","author":"Youssef","year":"2023","journal-title":"Am. J. Bioeth."},{"issue":"6711","key":"10.1016\/j.bspc.2025.109285_b2295","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1126\/science.adq1977","article-title":"AI and biosecurity: the need for governance","volume":"385","author":"Bloomfield","year":"2024","journal-title":"Science"},{"key":"10.1016\/j.bspc.2025.109285_b2300","doi-asserted-by":"crossref","unstructured":"N. A. Smuha, \u201cRegulation 2024\/1689 of the Eur. Parl. & Council of June 13, 2024 (Eu Artificial Intelligence Act),\u201d International Legal Materials, pp. 1-148, 2025, doi: 10.1017\/ilm.2024.46.","DOI":"10.1017\/ilm.2024.46"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b2305","doi-asserted-by":"crossref","first-page":"314","DOI":"10.17564\/2316-3801.2024v12n2p314-326","article-title":"Ethical challenges of artificial intelligence in the light of human rights","volume":"12","author":"Barreto","year":"2024","journal-title":"Interfaces Cient\u00edficas-Humanas e Sociais"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2310","first-page":"1","article-title":"Civil liability for the actions of autonomous AI in healthcare: an invitation to further contemplation","volume":"11","author":"Eldakak","year":"2024","journal-title":"Humanities and Social Sci. Commun."},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b2315","doi-asserted-by":"crossref","first-page":"e428","DOI":"10.1016\/S2589-7500(24)00061-X","article-title":"Ethical and regulatory challenges of large language models in medicine","volume":"6","author":"Ong","year":"2024","journal-title":"The Lancet Digital Health"},{"key":"10.1016\/j.bspc.2025.109285_b2320","doi-asserted-by":"crossref","DOI":"10.1056\/AIra2400038","article-title":"Medical ethics of large language models in medicine","author":"Ong","year":"2024","journal-title":"NEJM AI"},{"issue":"1s","key":"10.1016\/j.bspc.2025.109285_b2325","doi-asserted-by":"crossref","first-page":"393","DOI":"10.52783\/jes.780","article-title":"Autonomous healthcare systems: deep learning-based iot solutions for continuous monitoring and adaptive treatment","volume":"20","author":"Sambare","year":"2024","journal-title":"J. Electrical Sys."},{"key":"10.1016\/j.bspc.2025.109285_b2330","first-page":"1","article-title":"Should the use of adaptive machine learning systems in medicine be classified as research?","author":"Sparrow","year":"2024","journal-title":"Am. J. Bioeth."},{"key":"10.1016\/j.bspc.2025.109285_b2335","doi-asserted-by":"crossref","unstructured":"N. F. A. Bakar, H. L. Tan, Y. P. Lim, N. Adrus, and J. Abdullah, \u201cEnvironmental impact of quantum dots,\u201d. In: Graphene, Nanotubes and Quantum Dots-Based Nanotechnology: Elsevier, 2022, pp. 837-867.","DOI":"10.1016\/B978-0-323-85457-3.00011-6"},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b2340","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3390\/biotech13020014","article-title":"Challenges for the post-market environmental monitoring in the european union imposed by novel applications of genetically modified and genome-edited organisms","volume":"13","author":"Dolezel","year":"2024","journal-title":"Biotech"},{"key":"10.1016\/j.bspc.2025.109285_b2345","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.163712","article-title":"The screening and prioritization of contaminants of emerging concern in the marine environment based on multiple biological response measures","volume":"886","author":"James","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b2350","doi-asserted-by":"crossref","first-page":"2432","DOI":"10.1021\/acssensors.3c00961","article-title":"Let\u2019s talk about slime; or why biofouling needs more attention in sensor science","volume":"8","author":"Koren","year":"2023","journal-title":"ACS Sensors"},{"key":"10.1016\/j.bspc.2025.109285_b2355","article-title":"Safety aspects of microorganisms deliberately released into the environment","author":"Lensch","year":"2023","journal-title":"EFB Bioeconomy J."},{"key":"10.1016\/j.bspc.2025.109285_b2360","article-title":"Review on fate, transport, toxicity and health risk of nanoparticles in natural ecosystems: emerging challenges in the modern age and solutions toward a sustainable environment","author":"Tran","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"2","key":"10.1016\/j.bspc.2025.109285_b2365","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.synbio.2022.03.005","article-title":"Regulation and management of the biosecurity for synthetic biology","volume":"7","author":"Zeng","year":"2022","journal-title":"Synth. Syst. Biotechnol."},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b2370","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1089\/apb.2023.0007","article-title":"Environmental health and safety offers a biosafety risk assessment for a theoretical model of a gene therapy process transfer from research and development to large-scale manufacturing","volume":"28","author":"Godwin","year":"2023","journal-title":"Appl. Biosaf."},{"key":"10.1016\/j.bspc.2025.109285_b2375","doi-asserted-by":"crossref","DOI":"10.2903\/j.efsa.2022.e200407","article-title":"Improving the risk assessment of antimicrobial resistance (AMR) along the food\/feed chain and from environmental reservoirs using qMRA and probabilistic modelling","volume":"20","author":"Niegowska","year":"2022","journal-title":"EFSA J."},{"key":"10.1016\/j.bspc.2025.109285_b2380","doi-asserted-by":"crossref","DOI":"10.3389\/ftox.2023.1176745","article-title":"Expanding adverse outcome pathways towards one health models for nanosafety","volume":"5","author":"Saarim\u00e4ki","year":"2023","journal-title":"Front. Toxicol."},{"key":"10.1016\/j.bspc.2025.109285_b2385","unstructured":"J. Chen, V. Storchan, and E. Kurshan, \u201cBeyond fairness metrics: Roadblocks and challenges for ethical ai in practice,\u201d arXiv preprint arXiv:2108.06217, 2021."},{"key":"10.1016\/j.bspc.2025.109285_b2390","doi-asserted-by":"crossref","DOI":"10.23889\/ijpds.v8i3.2279","article-title":"Using data hazards to support safe and ethical digital footprint research","volume":"8","author":"Di Cara","year":"2023","journal-title":"Int. J. Population Data Sci."},{"key":"10.1016\/j.bspc.2025.109285_b2395","article-title":"Legal and ethical issues associated with challenges in the implementation of the electronic medical record system and its current laws in India","volume":"16","author":"Janarthanan","year":"2024","journal-title":"Cureus."},{"key":"10.1016\/j.bspc.2025.109285_b2400","article-title":"Ethical and privacy considerations in medical big data: balancing innovation and safeguarding Patient","volume":"5","author":"Mandeep Singh","year":"2024","journal-title":"Int. J. Res. Publication and Rev."},{"key":"10.1016\/j.bspc.2025.109285_b2405","doi-asserted-by":"crossref","unstructured":"N. Suresh, A. Selvakumar, G. Sridhar, and S. Catherine, \u201cEthical Considerations in AI Implementation for Patient Data Security and Privacy,\u201d. In: AI Healthcare Applications and Security, Ethical, and Legal Considerations: IGI Global, 2024, pp. 139-147.","DOI":"10.4018\/979-8-3693-7452-8.ch008"},{"issue":"3","key":"10.1016\/j.bspc.2025.109285_b2410","doi-asserted-by":"crossref","DOI":"10.3390\/informatics11030058","article-title":"Ethical challenges and solutions of generative AI: an interdisciplinary perspective","volume":"11","author":"Al-kfairy","year":"2024","journal-title":"Informatics"},{"issue":"6","key":"10.1016\/j.bspc.2025.109285_b2415","doi-asserted-by":"crossref","first-page":"2018","DOI":"10.22214\/ijraset.2024.63445","article-title":"Regulatory frameworks for ethical AI development in coding","volume":"12","author":"Shivagouda","year":"2024","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol. (IJRASET)"},{"key":"10.1016\/j.bspc.2025.109285_b2420","doi-asserted-by":"crossref","unstructured":"N. Agarwal, Nupur, P. K. Paul, and S. K. Mishra, \u201cArtificial Intelligence and Machine Learning for Analysis of Multi-omics,\u201d. In: Multi-Omics Analysis of the Human Microbiome: From Technology to Clinical Applications: Springer, 2024, pp. 339-354.","DOI":"10.1007\/978-981-97-1844-3_16"},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b2425","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0307482","article-title":"A framework for block-wise missing data in multi-omics","volume":"19","author":"Baena-Miret","year":"2024","journal-title":"PLoS One"},{"issue":"4","key":"10.1016\/j.bspc.2025.109285_b2430","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3390\/proteomes11040034","article-title":"Multi-omics integration for the design of novel therapies and the identification of novel biomarkers","volume":"11","author":"Ivanisevic","year":"2023","journal-title":"Proteomes"},{"key":"10.1016\/j.bspc.2025.109285_b2435","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2024.104629","article-title":"Computational frameworks integrating deep learning and statistical models in mining multimodal omics data","author":"Lac","year":"2024","journal-title":"J. Biomed. Inform."},{"issue":"7","key":"10.1016\/j.bspc.2025.109285_b2440","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.3390\/biomedicines12071496","article-title":"Navigating challenges and opportunities in multi-omics integration for personalized healthcare","volume":"12","author":"Mohr","year":"2024","journal-title":"Biomedicines"},{"key":"10.1016\/j.bspc.2025.109285_b2445","doi-asserted-by":"crossref","DOI":"10.1146\/annurev-biodatasci-102523-103801","article-title":"Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine","volume":"7","author":"Nam","year":"2024","journal-title":"Annu. Rev. Biomed. Data Sci."},{"key":"10.1016\/j.bspc.2025.109285_b2450","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2024.129612","article-title":"A hybrid forecasting framework based on MCS and machine learning for higher dimensional and unbalanced systems","volume":"637","author":"Yang","year":"2024","journal-title":"Physica A"},{"key":"10.1016\/j.bspc.2025.109285_b2455","doi-asserted-by":"crossref","first-page":"45","DOI":"10.53469\/jtpes.2024.04(02).07","article-title":"Utilizing AI-enhanced multi-omics integration for predictive modeling of disease susceptibility in functional phenotypes","volume":"4","author":"Zhou","year":"2024","journal-title":"J. Theory and Practice of Eng. Sci."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2460","doi-asserted-by":"crossref","DOI":"10.1080\/19490976.2023.2297860","article-title":"Multi-omic approaches for host-microbiome data integration","volume":"16","author":"Chetty","year":"2024","journal-title":"Gut Microbes"},{"key":"10.1016\/j.bspc.2025.109285_b2465","doi-asserted-by":"crossref","unstructured":"T. Dang et al., \u201cAn integrative framework of stochastic variational variable selection for joint analysis of multi-omics microbiome data,\u201d bioRxiv, p. 2023.08. 18.553796, 2023.","DOI":"10.1101\/2023.08.18.553796"},{"key":"10.1016\/j.bspc.2025.109285_b2470","doi-asserted-by":"crossref","first-page":"4960","DOI":"10.1016\/j.csbj.2023.10.002","article-title":"Predicting metabolic fluxes from omics data via machine learning: moving from knowledge-driven towards data-driven approaches","volume":"21","author":"Gon\u00e7alves","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"15","key":"10.1016\/j.bspc.2025.109285_b2475","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.3390\/cells12151998","article-title":"Integration of meta-multi-omics data using probabilistic graphs and external knowledge","volume":"12","author":"Can","year":"2023","journal-title":"Cells"},{"key":"10.1016\/j.bspc.2025.109285_b2480","article-title":"Integrative analysis of multi-omics data with deep learning: challenges and opportunities in bioinformatics","volume":"44","author":"Gonesh Chandra Saha","year":"2023","journal-title":"Tuijin Jishu\/J. Propulsion Technol."},{"key":"10.1016\/j.bspc.2025.109285_b2485","series-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management","first-page":"5259","article-title":"MODIMO: workshop on multi-omics data integration for modelling biological systems","author":"Avesani","year":"2023"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2490","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1038\/s41467-024-46888-3","article-title":"Multi-omic integration of microbiome data for identifying disease-associated modules","volume":"15","author":"Muller","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bspc.2025.109285_b2495","article-title":"Advancements in cloud-based machine learning: navigating deployment and scalability","volume":"5","author":"Jindal","year":"2023","journal-title":"Int. J. Multidisciplinary Res."},{"key":"10.1016\/j.bspc.2025.109285_b2500","first-page":"171","article-title":"Machine learning algorithms scaling on large-scale data infrastructure","volume":"3","author":"Padmanaban","year":"2024","journal-title":"J. Artificial Intelligence General Sci. (JAIGS."},{"key":"10.1016\/j.bspc.2025.109285_b2505","doi-asserted-by":"crossref","DOI":"10.1016\/j.tem.2024.02.018","article-title":"Emerging methods for genome-scale metabolic modeling of microbial communities","author":"Tarzi","year":"2024","journal-title":"Trends in Endocrinology & Metabolism"},{"key":"10.1016\/j.bspc.2025.109285_b2510","series-title":"International Conference on Software Quality","first-page":"112","article-title":"ML-enabled systems model deployment and monitoring: status quo and problems","author":"Zimelewicz","year":"2024"},{"key":"10.1016\/j.bspc.2025.109285_b2515","series-title":"Scale-up and Chemical Process for Microbial Production of Plant-Derived Bioactive Compounds","first-page":"29","author":"Chen","year":"2024"},{"issue":"30","key":"10.1016\/j.bspc.2025.109285_b2520","first-page":"13171","article-title":"Quorum quenching in membrane bioreactors for fouling retardation: complexity provides opportunities","volume":"58","author":"Xu","year":"2024","journal-title":"Environ. Sci. Technol."},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2525","doi-asserted-by":"crossref","DOI":"10.1016\/j.joi.2024.101491","article-title":"Scientists\u2019 disciplinary characteristics and collaboration behaviour under the convergence paradigm: a multilevel network perspective","volume":"18","author":"Li","year":"2024","journal-title":"J. Informet."},{"key":"10.1016\/j.bspc.2025.109285_b2530","article-title":"Integrative approaches to tackle multidisciplinary challenges: a review of multi-science problem analysis","volume":"17","author":"Harle","year":"2024","journal-title":"Current Mater. Sci. (Bentham Sci.)"},{"key":"10.1016\/j.bspc.2025.109285_b2535","doi-asserted-by":"crossref","unstructured":"M. Boon, \u201cInterdisciplinarity through modelling,\u201d. In: The Routledge Handbook of Philosophy of Scientific Modeling. Routledge, 2024, pp. 395-411.","DOI":"10.4324\/9781003205647-34"},{"key":"10.1016\/j.bspc.2025.109285_b2540","doi-asserted-by":"crossref","DOI":"10.2196\/48297","article-title":"Machine learning\u2013enabled clinical information systems using fast healthcare interoperability resources data standards: scoping review","volume":"11","author":"Balch","year":"2023","journal-title":"JMIR Med. Inform."},{"key":"10.1016\/j.bspc.2025.109285_b2545","series-title":"2022 IEEE 30th International Requirements Engineering Conference (RE)","first-page":"219","article-title":"Evidence-driven data requirements engineering and data uncertainty assessment of machine learning-based safety-critical Systems","author":"Dey","year":"2022"},{"issue":"1","key":"10.1016\/j.bspc.2025.109285_b2550","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1007\/s10898-012-9882-7","article-title":"Integrated experimental design and nonlinear optimization to handle computationally expensive models under resource constraints","volume":"57","author":"Pint\u00e9r","year":"2013","journal-title":"J. Glob. Optim."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809425017963?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809425017963?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T16:59:29Z","timestamp":1768841969000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809425017963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":510,"alternative-id":["S1746809425017963"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2025.109285","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine Learning-assisted Quorum Sensing Monitoring and Control Systems for Precision Gene Regulation: Revolutionizing Synthetic Biology and Autonomous Therapeutic Applications","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2025.109285","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109285"}}