{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:13:22Z","timestamp":1780481602517,"version":"3.54.1"},"reference-count":113,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":24,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Grant Scheme","award":["FP019-2022"],"award-info":[{"award-number":["FP019-2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The Coronavirus Disease 2019 (COVID-19) pandemic has highlighted the significance of reliable molecular biomarkers in clinical use. Despite the popularity of traditional statistical approaches, the high dimensionality of transcriptomic data presents challenges for these conventional methods. While artificial intelligence (AI) algorithms have emerged as highly advantageous for handling these complex datasets, there is a lack of evaluation of these approaches in COVID-19 transcriptomic studies. This review aims to provide an evaluation of these studies employed for transcriptomic biomarker discovery in COVID-19 using AI, assessing their study designs, methodologies, and outcomes. Based on a comprehensive search for literature across five databases including Web of Science Core Collection, Scopus, PubMed\/MEDLINE, IEEE Xplore Digital Library, and LitCovid from December 2019 to March 2025, this review selected 63 studies for a narrative synthesis of four key sections: (i) The Landscape of AI-Driven COVID-19 Transcriptomics, (ii) Limitations of Studies, (iii) A Proposed AI-Driven Transcriptomics Framework, and (iv) Clinical Translation Challenges, Opportunities, and Future Directions. Our analysis revealed limitations in data quality, sample size, and heterogeneity, as well as methodologies regarding validation and interpretability. Thus, we proposed an evidence-informed workflow that addresses these current limitations in study design, while acknowledging real-world constraints. We further discuss the emerging potential of agentic AI systems as a promising solution to current limitations. By bridging methodological gaps with translation considerations, this review can enhance pandemic response strategies for future emerging infectious diseases.<\/jats:p>\n                  <jats:p>Key Points Applications observed in reviewed studies mainly included applications in diagnosis and severity stratification of COVID-19 patients. The limitations of current studies included small sample sizes, the reliance on public datasets lacking detailed metadata, batch effects and data heterogeneity reducing model robustness, the lack of external validation, risks of data leakage and circular validation leading to inflated performance metrics, and challenges in model interpretability. An evidence-informed AI-driven framework is proposed, acknowledging real-world constraints including small pandemic cohort sizes, domain shift from viral evolution, and resource-limited settings, with emerging agentic AI systems offering potential solutions.<\/jats:p>","DOI":"10.1093\/bib\/bbag249","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T11:31:30Z","timestamp":1777980690000},"source":"Crossref","is-referenced-by-count":0,"title":["Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19)"],"prefix":"10.1093","volume":"27","author":[{"given":"Li Ying","family":"Khoo","sequence":"first","affiliation":[{"name":"Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya , Lembah Pantai, 50603 Kuala Lumpur ,","place":["Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1922-2044","authenticated-orcid":false,"given":"Sarinder Kaur","family":"Dhillon","sequence":"additional","affiliation":[{"name":"Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, Universiti Malaya , Lembah Pantai, 50603 Kuala Lumpur ,","place":["Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"2026060305313358200_ref1","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1038\/nrc2294","article-title":"The properties of high-dimensional data spaces: implications for exploring gene and protein expression data","volume":"8","author":"Clarke","year":"2008","journal-title":"Nat Rev Cancer"},{"key":"2026060305313358200_ref2","doi-asserted-by":"publisher","first-page":"1206","DOI":"10.1038\/s41586-025-10014-0","article-title":"Advancing regulatory variant effect prediction with AlphaGenome","volume":"649","author":"Avsec","year":"2026","journal-title":"Nature"},{"key":"2026060305313358200_ref3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1093\/bib\/bbad319","article-title":"Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection","volume":"24","author":"Le","year":"2023","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1093\/bib\/bbae548","article-title":"Progress and opportunities of foundation models in bioinformatics","volume":"25","author":"Li","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref5","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.csbj.2019.09.005","article-title":"Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture","volume":"17","author":"Le","year":"2019","journal-title":"Comput Struct Biotechnol J"},{"key":"2026060305313358200_ref6","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/s42979-022-01043-x","article-title":"AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems","volume":"3","author":"Sarker","year":"2022","journal-title":"SN Comput Sci"},{"key":"2026060305313358200_ref7","doi-asserted-by":"publisher","first-page":"150225","DOI":"10.1016\/j.bbrc.2024.150225","article-title":"Machine learning and related approaches in transcriptomics","volume":"724","author":"Cheng","year":"2024","journal-title":"Biochem Biophys Res Commun"},{"key":"2026060305313358200_ref8","doi-asserted-by":"publisher","first-page":"100751","DOI":"10.1016\/j.cosrev.2025.100751","article-title":"Artificial intelligence in COVID-19 research: a comprehensive survey of innovations, challenges, and future directions","volume":"57","author":"Annan","year":"2025","journal-title":"Comput Sci Rev"},{"key":"2026060305313358200_ref9","doi-asserted-by":"publisher","first-page":"100095","DOI":"10.1016\/j.imj.2024.100095","article-title":"Innovative applications of artificial intelligence during the COVID-19 pandemic","volume":"3","author":"Lv","year":"2024","journal-title":"Infect Med"},{"key":"2026060305313358200_ref10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pbiomolbio.2023.02.003","article-title":"A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information","volume":"179","author":"Sekaran","year":"2023","journal-title":"Prog Biophys Mol Biol"},{"key":"2026060305313358200_ref11","doi-asserted-by":"publisher","first-page":"100564","DOI":"10.1016\/j.imu.2021.100564","article-title":"Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: a review","volume":"24","author":"Alballa","year":"2021","journal-title":"Inform Med Unlocked"},{"key":"2026060305313358200_ref12","doi-asserted-by":"publisher","first-page":"e23811","DOI":"10.2196\/23811","article-title":"Role of machine learning techniques to tackle the COVID-19 crisis: systematic review","volume":"9","author":"Syeda","year":"2021","journal-title":"JMIR Med Inform"},{"key":"2026060305313358200_ref13","doi-asserted-by":"publisher","first-page":"115695","DOI":"10.1016\/j.eswa.2021.115695","article-title":"Applications of artificial intelligence in COVID-19 pandemic: a comprehensive review","volume":"185","author":"Khan","year":"2021","journal-title":"Expert Syst Appl"},{"key":"2026060305313358200_ref14","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3389\/fmed.2021.704256","article-title":"Artificial intelligence for COVID-19: a systematic review","volume":"8","author":"Wang","year":"2021","journal-title":"Front Med"},{"key":"2026060305313358200_ref15","doi-asserted-by":"publisher","first-page":"110337","DOI":"10.1016\/j.chaos.2020.110337","article-title":"A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic","volume":"141","author":"Rasheed","year":"2020","journal-title":"Chaos, Solitons Fractals"},{"key":"2026060305313358200_ref16","doi-asserted-by":"publisher","first-page":"110059","DOI":"10.1016\/j.chaos.2020.110059","article-title":"Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: a review","volume":"139","author":"Lalmuanawma","year":"2020","journal-title":"Chaos, Solitons Fractals"},{"key":"2026060305313358200_ref17","doi-asserted-by":"publisher","first-page":"109581","DOI":"10.1109\/ACCESS.2020.3001973","article-title":"Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment","volume":"8","author":"Jamshidi","year":"2020","journal-title":"IEEE Access"},{"key":"2026060305313358200_ref18","doi-asserted-by":"publisher","first-page":"22071","DOI":"10.1073\/pnas.1900654116","article-title":"Definitions, methods, and applications in interpretable machine learning","volume":"116","author":"Murdoch","year":"2019","journal-title":"Proc Natl Acad Sci"},{"key":"2026060305313358200_ref19","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.tibtech.2017.02.012","article-title":"Why batch effects matter in omics data, and how to avoid them","volume":"35","author":"Goh","year":"2017","journal-title":"Trends Biotechnol"},{"key":"2026060305313358200_ref20","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13059-016-0881-8","article-title":"A survey of best practices for RNA-seq data analysis","volume":"17","author":"Conesa","year":"2016","journal-title":"Genome Biol"},{"key":"2026060305313358200_ref21","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1038\/nrg2825","article-title":"Tackling the widespread and critical impact of batch effects in high-throughput data","volume":"11","author":"Leek","year":"2010","journal-title":"Nat Rev Genet"},{"key":"2026060305313358200_ref22","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab120","article-title":"Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19","volume":"22","author":"Auwul","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref23","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1126\/sciadv.abe5984","article-title":"A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood","volume":"7","author":"Ng","year":"2021","journal-title":"Sci Adv"},{"key":"2026060305313358200_ref24","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.677025","article-title":"Biomarkers and immune repertoire metrics identified by peripheral blood transcriptomic sequencing reveal the pathogenesis of COVID-19","volume":"12","author":"Liu","year":"2021","journal-title":"Front Immunol"},{"key":"2026060305313358200_ref25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/2203636","article-title":"Identification of novel COVID-19 biomarkers by multiple feature selection strategies","volume":"2021","author":"Zhang","year":"2021","journal-title":"Comput Math Methods Med"},{"key":"2026060305313358200_ref26","doi-asserted-by":"publisher","first-page":"8","DOI":"10.3389\/fcell.2020.627302","article-title":"Identifying transcriptomic signatures and rules for SARS-CoV-2 infection","volume":"8","author":"Zhang","year":"2021","journal-title":"Front Cell Dev Biol"},{"key":"2026060305313358200_ref27","doi-asserted-by":"publisher","first-page":"4864","DOI":"10.1609\/aaai.v35i6.16619","article-title":"Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and graph neural networks","volume-title":"Thirty-Fifth AAAI Conference on Artificial Intelligence, Thirty-Third Conference on Innovative Applications of Artificial Intelligence and the Eleventh Symposium on Educational Advances in Artificial Intelligence","author":"Sehanobish","year":"2021"},{"key":"2026060305313358200_ref28","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.cels.2020.10.003","article-title":"Large-scale multi-omic analysis of COVID-19 severity","volume":"12","author":"Overmyer","year":"2021","journal-title":"Cell Syst"},{"key":"2026060305313358200_ref29","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2021.705646","article-title":"On deep landscape exploration of COVID-19 patients cells and severity markers","volume":"12","author":"V\u00e1zquez-Jim\u00e9nez","year":"2021","journal-title":"Front Immunol"},{"key":"2026060305313358200_ref30","doi-asserted-by":"publisher","first-page":"102596","DOI":"10.1016\/j.jaut.2021.102596","article-title":"Profiling of the immune repertoire in COVID-19 patients with mild, severe, convalescent, or retesting-positive status","volume":"118","author":"Zhou","year":"2021","journal-title":"J Autoimmun"},{"key":"2026060305313358200_ref31","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/genes13091602","article-title":"A novel 3-gene signature for identifying COVID-19 patients based on bioinformatics and machine learning","volume":"13","author":"Lai","year":"2022","journal-title":"Genes"},{"key":"2026060305313358200_ref32","doi-asserted-by":"publisher","DOI":"10.1128\/msystems.00671-22","article-title":"A 2-gene host signature for improved accuracy of COVID-19 diagnosis agnostic to viral variants","volume":"8","author":"Albright","year":"2022","journal-title":"mSystems"},{"key":"2026060305313358200_ref33","doi-asserted-by":"publisher","DOI":"10.3390\/v16071029","article-title":"A T-cell-derived 3-gene signature distinguishes SARS-CoV-2 from common respiratory viruses","volume":"16","author":"Li","year":"2024","journal-title":"Viruses-Basel"},{"key":"2026060305313358200_ref34","doi-asserted-by":"publisher","first-page":"104309","DOI":"10.1016\/j.isci.2022.104309","article-title":"Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection","volume":"25","author":"Zeng","year":"2022","journal-title":"ISCIENCE"},{"key":"2026060305313358200_ref35","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/cells11050847","article-title":"Severe COVID-19 shares a common neutrophil activation signature with other acute inflammatory states","volume":"11","author":"Schimke","year":"2022","journal-title":"Cells"},{"key":"2026060305313358200_ref36","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.1053772","article-title":"Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8+ T cells","volume":"13","author":"Lu","year":"2022","journal-title":"Front Genet"},{"key":"2026060305313358200_ref37","doi-asserted-by":"publisher","first-page":"1001070","DOI":"10.3389\/fimmu.2022.1001070","article-title":"Exploration of blood-derived coding and non-coding RNA diagnostic immunological panels for COVID-19 through a co-expressed-based machine learning procedure","volume":"13","author":"Zarei Ghobadi","year":"2022","journal-title":"Front Immunol"},{"key":"2026060305313358200_ref38","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.871164","article-title":"Unraveling T cell responses for long term protection of SARS-CoV-2 infection","volume":"13","author":"Wu","year":"2022","journal-title":"Front Genet"},{"key":"2026060305313358200_ref39","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/genes13122264","article-title":"Characterizing macrophages diversity in COVID-19 patients using deep learning","volume":"13","author":"Flores","year":"2022","journal-title":"Genes (Basel)"},{"key":"2026060305313358200_ref40","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3389\/fmolb.2022.952626","article-title":"Identification of COVID-19-specific immune markers using a machine learning method","volume":"9","author":"Li","year":"2022","journal-title":"Front Mol Biosci"},{"key":"2026060305313358200_ref41","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2023.1152223","article-title":"Identification of cuproptosis-related molecular subtypes and a novel predictive model of COVID-19 based on machine learning","volume":"14","author":"Luo","year":"2023","journal-title":"Front Immunol"},{"key":"2026060305313358200_ref42","doi-asserted-by":"publisher","first-page":"11769343231153293","DOI":"10.1177\/11769343231153293","article-title":"The biological processes of ferroptosis involved in pathogenesis of COVID-19 and core ferroptoic genes related with the occurrence and severity of this disease","volume":"19","author":"Zhang","year":"2023","journal-title":"Evol Bioinforma"},{"key":"2026060305313358200_ref43","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2023.1191004","article-title":"Immunogenic cell death-led discovery of COVID-19 biomarkers and inflammatory infiltrates","volume":"14","author":"Zhuo","year":"2023","journal-title":"Front Microbiol"},{"key":"2026060305313358200_ref44","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41598-024-59117-0","article-title":"Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19","volume":"14","author":"Li","year":"2024","journal-title":"Sci Rep"},{"key":"2026060305313358200_ref45","doi-asserted-by":"publisher","DOI":"10.3390\/v16060923","article-title":"Identification and analysis of biomarkers associated with lipophagy and therapeutic agents for COVID-19","volume":"16","author":"Wu","year":"2024","journal-title":"Viruses"},{"key":"2026060305313358200_ref46","doi-asserted-by":"publisher","first-page":"155784","DOI":"10.1016\/j.phymed.2024.155784","article-title":"Computational identification of mitochondrial dysfunction biomarkers in severe SARS-CoV-2 infection: facilitating therapeutic applications of phytomedicine","volume":"131","author":"Zhang","year":"2024","journal-title":"Phytomedicine"},{"key":"2026060305313358200_ref47","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1101\/gr.278439.123","article-title":"A gene regulatory network-aware graph learning method for cell identity annotation in single-cell RNA-seq data","volume":"34","author":"Zhao","year":"2024","journal-title":"Genome Res"},{"key":"2026060305313358200_ref48","doi-asserted-by":"publisher","first-page":"e2412402","DOI":"10.1002\/advs.202412402","article-title":"A knowledge-guided graph learning approach bridging phenotype- and target-based drug discovery","volume":"12","author":"Ye","year":"2025","journal-title":"Adv Sci"},{"key":"2026060305313358200_ref49","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae112","article-title":"scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics","volume":"25","author":"Zheng","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref50","doi-asserted-by":"publisher","first-page":"100563","DOI":"10.1016\/j.crmeth.2023.100563","article-title":"Single-cell multi-omics topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures","volume":"3","author":"Zhou","year":"2023","journal-title":"Cell Rep Methods"},{"key":"2026060305313358200_ref51","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1186\/s13059-023-03049-x","article-title":"DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection","volume":"24","author":"Hausmann","year":"2023","journal-title":"Genome Biol"},{"key":"2026060305313358200_ref52","doi-asserted-by":"publisher","first-page":"3","DOI":"10.4274\/balkanmedj.galenos.2022.2022-11-51","article-title":"Must-have qualities of clinical research on artificial intelligence and machine learning","volume":"40","author":"Ko\u00e7ak","year":"2023","journal-title":"Balkan Med J"},{"key":"2026060305313358200_ref53","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3390\/v15010104","article-title":"A counterintuitive neutrophil-mediated pattern in COVID-19 patients revealed through transcriptomics analysis","volume":"15","author":"\u00d6zbek","year":"2023","journal-title":"Viruses-Basel"},{"key":"2026060305313358200_ref54","doi-asserted-by":"publisher","first-page":"1251067","DOI":"10.3389\/fimmu.2023.1251067","article-title":"The two-stage molecular scenery of SARS-CoV-2 infection with implications to disease severity: an in-silico quest","volume":"14","author":"Potamias","year":"2023","journal-title":"Front Immunol"},{"key":"2026060305313358200_ref55","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s13167-023-00317-5","article-title":"Multi-omics and immune cells\u2019 profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of predictive, preventive, and personalized medicine","volume":"14","author":"Zhu","year":"2023","journal-title":"EPMA J"},{"key":"2026060305313358200_ref56","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s42003-023-04447-4","article-title":"Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity","volume":"6","author":"Park","year":"2023","journal-title":"Commun Biol"},{"key":"2026060305313358200_ref57","doi-asserted-by":"publisher","DOI":"10.3390\/ijms24076250","article-title":"Integrating AI\/ML models for patient stratification leveraging omics dataset and clinical biomarkers from COVID-19 patients: a promising approach to personalized medicine","volume":"24","author":"Bello","year":"2023","journal-title":"Int J Mol Sci"},{"key":"2026060305313358200_ref58","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-023-01703-6","article-title":"SARS-CoV-2 diagnosis using transcriptome data: a machine learning approach","volume":"4","author":"Jeyananthan","year":"2023","journal-title":"SN Comput Sci"},{"key":"2026060305313358200_ref59","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s12920-022-01222-y","article-title":"Co-expression analysis to identify key modules and hub genes associated with COVID-19 in platelets","volume":"15","author":"Alarabi","year":"2022","journal-title":"BMC Med Genomics"},{"key":"2026060305313358200_ref60","doi-asserted-by":"publisher","first-page":"105684","DOI":"10.1016\/j.compbiomed.2022.105684","article-title":"Integrated COVID-19 predictor: differential expression analysis to reveal potential biomarkers and prediction of coronavirus using RNA-Seq profile data","volume":"147","author":"Iqbal","year":"2022","journal-title":"Comput Biol Med"},{"key":"2026060305313358200_ref61","doi-asserted-by":"publisher","first-page":"eabj7521","DOI":"10.1126\/scitranslmed.abj7521","article-title":"Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort","volume":"14","author":"Carapito","year":"2022","journal-title":"Sci Transl Med"},{"key":"2026060305313358200_ref62","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.molimm.2024.12.004","article-title":"Recognizing SARS-CoV-2 infection of nasopharyngeal tissue at the single-cell level by machine learning method","volume":"177","author":"Bao","year":"2025","journal-title":"Mol Immunol"},{"key":"2026060305313358200_ref63","volume-title":"2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2023 Dec 5\u20138; Istanbul, T\u00fcrkiye","author":"Li"},{"key":"2026060305313358200_ref64","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf136","article-title":"Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective","volume":"26","author":"Ge","year":"2025","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref65","doi-asserted-by":"publisher","first-page":"e0297664","DOI":"10.1371\/journal.pone.0297664","article-title":"Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage","volume":"19","author":"Chang","year":"2024","journal-title":"PLoS One"},{"key":"2026060305313358200_ref66","doi-asserted-by":"publisher","first-page":"100857","DOI":"10.1016\/j.landig.2025.01.013","article-title":"Importance of sample size on the quality and utility of AI-based prediction models for healthcare","volume":"7","author":"Riley","year":"2025","journal-title":"Lancet Digit Health"},{"key":"2026060305313358200_ref67","doi-asserted-by":"publisher","DOI":"10.3390\/ijms24054905","article-title":"Classification of COVID-19 patients into clinically relevant subsets by a novel machine learning pipeline using transcriptomic features","volume":"24","author":"Daamen","year":"2023","journal-title":"Int J Mol Sci"},{"key":"2026060305313358200_ref68","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1111\/bph.17392","article-title":"Plasma miR-1-3p levels predict severity in hospitalized COVID-19 patients","volume":"182","author":"Di Pietro","year":"2024","journal-title":"Br J Pharmacol"},{"key":"2026060305313358200_ref69","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3390\/biom12121735","article-title":"Identification of transcriptome biomarkers for severe COVID-19 with machine learning methods","volume":"12","author":"Li","year":"2022","journal-title":"Biomolecules"},{"key":"2026060305313358200_ref70","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/diagnostics14121284","article-title":"A machine learning model for the prediction of COVID-19 severity using RNA-Seq, clinical, and co-morbidity data","volume":"14","author":"Sethi","year":"2024","journal-title":"Diagnostics"},{"key":"2026060305313358200_ref71","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae482","article-title":"scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets","volume":"25","author":"Xie","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref72","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/s10528-023-10511-9","article-title":"Correction: a machine learning approach to identify potential miRNA-gene regulatory network contributing to the pathogenesis of SARS-CoV-2 infection","volume":"62","author":"Das","year":"2023","journal-title":"Biochem Genet"},{"key":"2026060305313358200_ref73","volume-title":"13TH ACM International Conference on Bioinformatics Computational Biology and Health Informatics, BCB","author":"Goel"},{"key":"2026060305313358200_ref74","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/s10020-022-00513-5","article-title":"Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood\/nasopharyngeal transcriptome meta-analysis","volume":"28","author":"Maleknia","year":"2022","journal-title":"Mol Med"},{"key":"2026060305313358200_ref75","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.926069","article-title":"XGBoost-based feature learning method for mining COVID-19 novel diagnostic markers","volume":"10","author":"Song","year":"2022","journal-title":"Front Public Health"},{"key":"2026060305313358200_ref76","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/life13041011","article-title":"Using machine learning methods in identifying genes associated with COVID-19 in cardiomyocytes and cardiac vascular endothelial cells","volume":"13","author":"Xu","year":"2023","journal-title":"Life-Basel"},{"key":"2026060305313358200_ref77","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1186\/s12859-019-2855-9","article-title":"Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset","volume":"20","author":"Somekh","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2026060305313358200_ref78","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s12920-024-02072-6","article-title":"Challenges of reproducible AI in biomedical data science","volume":"18","author":"Han","year":"2025","journal-title":"BMC Med Genomics"},{"key":"2026060305313358200_ref79","first-page":"20313","volume-title":"2023 IEEE\/CVF International Conference on Computer Vision (ICCV); 2023 Oct 1\u20136; Paris, France","author":"Gustafson"},{"key":"2026060305313358200_ref80","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/s41586-025-09716-2","article-title":"Fair human-centric image dataset for ethical AI benchmarking","volume":"648","author":"Xiang","year":"2025","journal-title":"Nature"},{"key":"2026060305313358200_ref81","doi-asserted-by":"publisher","first-page":"100804","DOI":"10.1016\/j.patter.2023.100804","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"Kapoor","year":"2023","journal-title":"Patterns"},{"key":"2026060305313358200_ref82","doi-asserted-by":"publisher","first-page":"112118","DOI":"10.1016\/j.jclinepi.2025.112118","article-title":"Adherence to TRIPOD+AI guideline: an updated reporting assessment tool","volume":"191","author":"Kanter","year":"2026","journal-title":"J Clin Epidemiol"},{"key":"2026060305313358200_ref83","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1038\/s41597-025-04451-9","article-title":"Applying the FAIR principles to computational workflows","volume":"12","author":"Wilkinson","year":"2025","journal-title":"Sci Data"},{"key":"2026060305313358200_ref84","doi-asserted-by":"publisher","first-page":"e28488","DOI":"10.1002\/jmv.28488","article-title":"Machine learning-driven blood transcriptome-based discovery of SARS-CoV-2 specific severity biomarkers","volume":"95","author":"Krishnamoorthy","year":"2023","journal-title":"J Med Virol"},{"key":"2026060305313358200_ref85","doi-asserted-by":"publisher","first-page":"15107","DOI":"10.1038\/s41598-021-94501-0","article-title":"Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets","volume":"11","author":"Papoutsoglou","year":"2021","journal-title":"Sci Rep"},{"key":"2026060305313358200_ref86","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3390\/e23010018","article-title":"Explainable AI: a review of machine learning interpretability methods","volume":"23","author":"Linardatos","year":"2020","journal-title":"Entropy (Basel)"},{"key":"2026060305313358200_ref87","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems; 2019 Dec 8\u201314, Vancouver, Canada","author":"Ying","year":"2019"},{"key":"2026060305313358200_ref88","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3390\/cancers15071969","article-title":"Empirical study of overfitting in deep learning for predicting breast cancer metastasis","volume":"15","author":"Xu","year":"2023","journal-title":"Cancers (Basel)"},{"key":"2026060305313358200_ref89","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3389\/fgene.2024.1381917","article-title":"In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data","volume":"15","author":"Ma","year":"2024","journal-title":"Front Genet"},{"key":"2026060305313358200_ref90","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab325","article-title":"DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data","volume":"22","author":"Chen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref91","doi-asserted-by":"publisher","first-page":"5599","DOI":"10.1038\/s41598-023-32268-2","article-title":"A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures","volume":"13","author":"Momeni","year":"2023","journal-title":"Sci Rep"},{"key":"2026060305313358200_ref92","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1012480","article-title":"BootCellNet, a resampling-based procedure, promotes unsupervised identification of cell populations via robust inference of gene regulatory networks","volume":"20","author":"Kumagai","year":"2024","journal-title":"PLoS Comput Biol"},{"key":"2026060305313358200_ref93","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.3390\/make6020065","article-title":"Cross-validation visualized: a narrative guide to advanced methods","volume":"6","author":"Allgaier","year":"2024","journal-title":"Mach Learn Knowl Extr"},{"key":"2026060305313358200_ref94","doi-asserted-by":"publisher","first-page":"11563","DOI":"10.1038\/s41598-024-62585-z","article-title":"Applying oversampling before cross-validation will lead to high bias in radiomics","volume":"14","author":"Demircio\u011flu","year":"2024","journal-title":"Sci Rep"},{"key":"2026060305313358200_ref95","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s10916-022-01807-1","article-title":"Domain shifts in machine learning based Covid-19 diagnosis from blood tests","volume":"46","author":"Roland","year":"2022","journal-title":"J Med Syst"},{"key":"2026060305313358200_ref96","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf576","article-title":"Artificial intelligence in bioinformatics: a survey","volume":"26","author":"Jiang","year":"2025","journal-title":"Brief Bioinform"},{"key":"2026060305313358200_ref97","doi-asserted-by":"publisher","first-page":"101882","DOI":"10.1016\/j.disamonth.2025.101882","article-title":"The integration of artificial intelligence into clinical medicine: trends, challenges, and future directions","volume":"71","author":"Aravazhi","year":"2025","journal-title":"Disease-a-Month"},{"key":"2026060305313358200_ref98","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1186\/s12916-019-1426-2","article-title":"Key challenges for delivering clinical impact with artificial intelligence","volume":"17","author":"Kelly","year":"2019","journal-title":"BMC Med"},{"key":"2026060305313358200_ref99","doi-asserted-by":"publisher","first-page":"2845","DOI":"10.3390\/diagnostics15222845","article-title":"Advances in point-of-care infectious disease diagnostics: integration of technologies, validation, artificial intelligence, and regulatory oversight","volume":"15","author":"Kardjadj","year":"2025","journal-title":"Diagnostics"},{"key":"2026060305313358200_ref100","doi-asserted-by":"publisher","first-page":"13","DOI":"10.22270\/ijdra.v10i4.545","article-title":"Regulatory prospective on software as a medical device","volume":"10","author":"Chothani","year":"2022","journal-title":"Int J Drug Regul Aff"},{"key":"2026060305313358200_ref101","article-title":"Software as a Medical Device (SaMD)","author":"IMDRF"},{"key":"2026060305313358200_ref102","doi-asserted-by":"publisher","first-page":"3165","DOI":"10.1038\/s41467-025-58527-6","article-title":"Machine learning in point-of-care testing: innovations, challenges, and opportunities","volume":"16","author":"Han","year":"2025","journal-title":"Nat Commun"},{"key":"2026060305313358200_ref103","doi-asserted-by":"publisher","first-page":"18912","DOI":"10.1109\/ACCESS.2025.3532853","article-title":"Agentic AI: autonomous intelligence for complex goals\u2014a comprehensive survey","volume":"13","author":"Acharya","year":"2025","journal-title":"IEEE Access"},{"key":"2026060305313358200_ref104","author":"Liu"},{"key":"2026060305313358200_ref105","doi-asserted-by":"publisher","first-page":"e2407094","DOI":"10.1002\/advs.202407094","article-title":"An AI agent for fully automated multi-omic analyses","volume":"11","author":"Zhou","year":"2024","journal-title":"Adv Sci"},{"key":"2026060305313358200_ref106","first-page":"2027","article-title":"Agentomics: an agentic system that autonomously develops novel state-of-the-art solutions for biomedical machine learning tasks","volume":"2026","author":"Martinek","year":"2001","journal-title":"bioRxiv"},{"key":"2026060305313358200_ref107","first-page":"2013","article-title":"CellAgent: LLM-driven multi-agent framework for natural language-based single-cell analysis","volume":"2025","author":"Xiao","year":"2005","journal-title":"bioRxiv"},{"key":"2026060305313358200_ref108","doi-asserted-by":"publisher","DOI":"10.1101\/2025.04.01.646731","article-title":"Spatial transcriptomics AI agent charts hPSC-pancreas maturation in vivo","author":"Lin","year":"2025","journal-title":"bioRxiv"},{"key":"2026060305313358200_ref109","first-page":"2028","article-title":"Autonomous AI agents discover aging interventions from millions of molecular profiles","volume":"2025","author":"Ying","year":"2002","journal-title":"bioRxiv"},{"key":"2026060305313358200_ref110","author":"Miao"},{"key":"2026060305313358200_ref111","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1038\/s44387-025-00064-0","article-title":"An agentic AI framework for ingestion and standardization of single-cell RNA-seq data analysis","volume":"2","author":"Nouri","year":"2026","journal-title":"npj Artif Intell"},{"key":"2026060305313358200_ref112","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.21275\/SR25424081718","article-title":"Causal inference in agentic AI: bridging explainability and dynamic decision making","volume":"14","author":"Chakrabarty","year":"2025","journal-title":"Int J Sci Res"},{"key":"2026060305313358200_ref113","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/3\/bbag249\/68381793\/bbag249.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/3\/bbag249\/68381793\/bbag249.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:31:42Z","timestamp":1780479102000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag249\/8692731"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":113,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5,4]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag249","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,5]]},"published":{"date-parts":[[2026,5]]},"article-number":"bbag249"}}