{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T03:29:53Z","timestamp":1793330993807,"version":"4.5.0"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-36585-0","type":"journal-article","created":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T18:35:09Z","timestamp":1769625309000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Diagnosis of Alzheimer\u2019s disease with high accuracy via Petri net modeling of signaling pathways"],"prefix":"10.1038","volume":"16","author":[{"given":"Hananeh","family":"Ebrahimian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Asadzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maseud","family":"Rahgozar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaveh","family":"Kavousi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"36585_CR1","doi-asserted-by":"crossref","unstructured":"Juganavar, A., Joshi, A. & Shegekar, T. Navigating Early Alzheimer\u2019s Diagnosis: A Comprehensive Review of Diagnostic Innovations. Cureus 15, (2023).","DOI":"10.7759\/cureus.44937"},{"key":"36585_CR2","doi-asserted-by":"publisher","first-page":"30294","DOI":"10.1038\/s41598-024-82208-x","volume":"14","author":"R Shukla","year":"2024","unstructured":"Shukla, R. & Singh, T. R. AlzGenPred - CatBoost-based gene classifier for predicting alzheimer\u2019s disease using high-throughput sequencing data. Sci. Rep. 14, 30294 (2024).","journal-title":"Sci. Rep."},{"key":"36585_CR3","first-page":"1170","volume":"10","author":"L Anwal","year":"2021","unstructured":"Anwal, L. A comprehensive review on alzheimer\u2019s disease. World J. Pharm. Pharm. Sci. 10, 1170 (2021).","journal-title":"World J. Pharm. Pharm. Sci."},{"key":"36585_CR4","doi-asserted-by":"publisher","first-page":"13954","DOI":"10.3390\/ijms232213954","volume":"23","author":"E Passeri","year":"2022","unstructured":"Passeri, E. et al. Alzheimer\u2019s disease: treatment strategies and their limitations. Int. J. Mol. Sci. 23, 13954 (2022).","journal-title":"Int. J. Mol. Sci."},{"key":"36585_CR5","doi-asserted-by":"publisher","first-page":"10728","DOI":"10.1038\/s41598-024-61363-1","volume":"14","author":"F Xu","year":"2024","unstructured":"Xu, F. et al. Diagnostic implications of ubiquitination-related gene signatures in alzheimer\u2019s disease. Sci. Rep. 14, 10728 (2024).","journal-title":"Sci. Rep."},{"key":"36585_CR6","doi-asserted-by":"publisher","first-page":"271","DOI":"10.3233\/JAD-215200","volume":"86","author":"J Schaffert","year":"2022","unstructured":"Schaffert, J. et al. Predictors of life expectancy in Autopsy-Confirmed alzheimer\u2019s Disease1. J. Alzheimer\u2019s Dis. 86, 271\u2013281 (2022).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"36585_CR7","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement. 15, 321\u2013387 (2019). (2019).","DOI":"10.1016\/j.jalz.2019.01.010"},{"key":"36585_CR8","doi-asserted-by":"crossref","unstructured":"Schaffert, J. et al. ZNF384: A potential therapeutic target for psoriasis and alzheimer\u2019s disease through inflammation and metabolism. Front Immunol 13, (2022).","DOI":"10.3389\/fimmu.2022.892368"},{"key":"36585_CR9","first-page":"1234","volume":"23","author":"W Li","year":"2018","unstructured":"Li, W., Zhao, Y., Chen, X., Xiao, Y. & Qin, Y. Detecting alzheimer\u2019s disease on small dataset: a knowledge transfer perspective. IEEE J. Biomed. Heal Inf. 23, 1234\u20131242 (2018).","journal-title":"IEEE J. Biomed. Heal Inf."},{"key":"36585_CR10","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1148\/radiol.2016162151","volume":"281","author":"R N Bryan","year":"2016","unstructured":"N Bryan, R. Machine learning applied to alzheimer disease. Radiology 281, 665\u2013668 (2016). at.","journal-title":"Radiology"},{"key":"36585_CR11","doi-asserted-by":"publisher","first-page":"6039","DOI":"10.1038\/s41598-024-56489-1","volume":"14","author":"ME AlMansoori","year":"2024","unstructured":"AlMansoori, M. E., Jemimah, S., Abuhantash, F. & AlShehhi, A. Predicting early alzheimer\u2019s with blood biomarkers and clinical features. Sci. Rep. 14, 6039 (2024).","journal-title":"Sci. Rep."},{"key":"36585_CR12","first-page":"48","volume":"2","author":"MS Teja","year":"2023","unstructured":"Teja, M. S., Thanuja, K., Deep, N. M., Reddy, P. R. & Reddy, O. L. K. Prediction and analysis of alzheimer\u2019s disease using deep learning algorithms. Int. J. Comput. Learn. \\& Intell. 2, 48\u201357 (2023).","journal-title":"Int. J. Comput. Learn. \\& Intell."},{"key":"36585_CR13","doi-asserted-by":"publisher","first-page":"186348","DOI":"10.1109\/ACCESS.2020.3029890","volume":"8","author":"MS Othman","year":"2020","unstructured":"Othman, M. S., Kumaran, S. R. & Yusuf, L. M. Gene selection using hybrid Multi-Objective cuckoo search algorithm with evolutionary operators for cancer microarray data. IEEE Access. 8, 186348\u2013186361 (2020).","journal-title":"IEEE Access."},{"key":"36585_CR14","doi-asserted-by":"publisher","first-page":"162","DOI":"10.3991\/ijim.v15i13.23227","volume":"15","author":"ST Ahmed","year":"2021","unstructured":"Ahmed, S. T., Kadhim, Q. K. & Alsultani, H. S. M. Abd almahdy, W. S. Applying the MCMSI for online educational systems using the Two-Factor authentication. Int. J. Interact. Mob. Technol. 15, 162 (2021).","journal-title":"Int. J. Interact. Mob. Technol."},{"key":"36585_CR15","doi-asserted-by":"publisher","unstructured":"Alenizi, A. S. & Al-Karawi, K. A. Internet of Things (IoT) Adoption: Challenges and Barriers. In: Proceedings of Seventh International Congress on Information and Communication Technology: ICICT 2022, London, Volume 3 217\u2013229 (2023). https:\/\/doi.org\/10.1007\/978-981-19-2394-4_20","DOI":"10.1007\/978-981-19-2394-4_20"},{"key":"36585_CR16","doi-asserted-by":"publisher","DOI":"10.4088\/PCC.12r01474","author":"DS Geldmacher","year":"2013","unstructured":"Geldmacher, D. S. & Kerwin, D. R. Practical diagnosis and management of dementia due to alzheimer\u2019s disease in the primary care setting. Prim. Care Companion CNS Disord. https:\/\/doi.org\/10.4088\/PCC.12r01474 (2013).","journal-title":"Prim. Care Companion CNS Disord"},{"key":"36585_CR17","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.12688\/f1000research.14506.1","volume":"7","author":"J Weller","year":"2018","unstructured":"Weller, J. & Budson, A. Current understanding of alzheimer\u2019s disease diagnosis and treatment. F1000Research 7, 1161 (2018).","journal-title":"F1000Research"},{"key":"36585_CR18","doi-asserted-by":"publisher","first-page":"371","DOI":"10.14283\/jpad.2021.23","volume":"8","author":"AP Porsteinsson","year":"2021","unstructured":"Porsteinsson, A. P., Isaacson, R. S., Knox, S., Sabbagh, M. N. & Rubino, I. Diagnosis of early alzheimer\u2019s disease: clinical practice in 2021. J. Prev. Alzheimer\u2019s Dis. 8, 371\u2013386 (2021).","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"36585_CR19","doi-asserted-by":"publisher","first-page":"2975","DOI":"10.3390\/diagnostics12122975","volume":"12","author":"R Hajjo","year":"2022","unstructured":"Hajjo, R., Sabbah, D. A., Abusara, O. H. & Al Bawab A. Q. A review of the recent advances in alzheimer\u2019s disease research and the utilization of network biology approaches for prioritizing diagnostics and therapeutics. Diagnostics 12, 2975 (2022).","journal-title":"Diagnostics"},{"key":"36585_CR20","doi-asserted-by":"crossref","unstructured":"Ashraf, J., Ahmad, J., Ali, A. & Ul-Haq, Z. Analyzing the behavior of neuronal pathways in alzheimer\u2019s disease using petri net modeling approach. Front Neuroinform 12, (2018).","DOI":"10.3389\/fninf.2018.00026"},{"key":"36585_CR21","doi-asserted-by":"publisher","first-page":"102651","DOI":"10.1016\/j.xpro.2023.102651","volume":"4","author":"F Liu","year":"2023","unstructured":"Liu, F., Heiner, M. & Gilbert, D. Protocol for biomodel engineering of unilevel to multilevel biological models using colored petri Nets. STAR. Protoc. 4, 102651 (2023).","journal-title":"STAR. Protoc."},{"key":"36585_CR22","doi-asserted-by":"publisher","first-page":"1329","DOI":"10.1016\/j.jalz.2015.02.006","volume":"11","author":"AT Kodamullil","year":"2015","unstructured":"Kodamullil, A. T., Younesi, E., Naz, M., Bagewadi, S. & Hofmann-Apitius, M. Computable cause\u2010and\u2010effect models of healthy and alzheimer\u2019s disease States and their mechanistic differential analysis. Alzheimer\u2019s Dement. 11, 1329\u20131339 (2015).","journal-title":"Alzheimer\u2019s Dement."},{"key":"36585_CR23","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.biosystems.2004.03.003","volume":"75","author":"M Heiner","year":"2004","unstructured":"Heiner, M., Koch, I. & Will, J. Model validation of biological pathways using petri nets\u2014demonstrated for apoptosis. Biosystems 75, 15\u201328 (2004).","journal-title":"Biosystems"},{"key":"36585_CR24","doi-asserted-by":"crossref","unstructured":"Koch, I., Reisig, W. & Schreiber, F. Modeling in Systems Biology: the Petri Net Approach16 (Springer science \\& business media, 2010).","DOI":"10.1007\/978-1-84996-474-6"},{"key":"36585_CR25","doi-asserted-by":"publisher","first-page":"3219","DOI":"10.3390\/app14083219","volume":"14","author":"A Rybarczyk","year":"2024","unstructured":"Rybarczyk, A., Formanowicz, D. & Formanowicz, P. The role of macrophage dynamics in atherosclerosis analyzed using a petri Net-Based model. Appl. Sci. 14, 3219 (2024).","journal-title":"Appl. Sci."},{"key":"36585_CR26","doi-asserted-by":"crossref","unstructured":"Puniya, B. L., Verma, M., Damiani, C., Bakr, S. & Dr\u00e4ger, A. Perspectives on computational modeling of biological systems and the significance of the SysMod community. Bioinforma Adv. 4, (2024).","DOI":"10.1093\/bioadv\/vbae090"},{"key":"36585_CR27","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/s12859-019-2635-6","volume":"20","author":"F Mansoori","year":"2019","unstructured":"Mansoori, F., Rahgozar, M. & Kavousi, K. FoPA: identifying perturbed signaling pathways in clinical conditions using formal methods. BMC Bioinform. 20, 92 (2019).","journal-title":"BMC Bioinform."},{"key":"36585_CR28","doi-asserted-by":"publisher","first-page":"C1126","DOI":"10.1152\/ajpcell.00487.2022","volume":"324","author":"I Koch","year":"2023","unstructured":"Koch, I. & B\u00fcttner, B. Computational modeling of signal transduction networks without kinetic parameters: petri net approaches. Am. J. Physiol. Physiol. 324, C1126\u2013C1140 (2023).","journal-title":"Am. J. Physiol. Physiol."},{"key":"36585_CR29","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1007\/s11047-010-9180-6","volume":"9","author":"P Baldan","year":"2010","unstructured":"Baldan, P., Cocco, N., Marin, A. & Simeoni, M. Petri Nets for modelling metabolic pathways: a survey. Nat. Comput. 9, 955\u2013989 (2010).","journal-title":"Nat. Comput."},{"key":"36585_CR30","doi-asserted-by":"publisher","first-page":"114073","DOI":"10.1016\/j.tcs.2023.114073","volume":"971","author":"VG Trinh","year":"2023","unstructured":"Trinh, V. G., Benhamou, B. & Soliman, S. Trap spaces of boolean networks are conflict-free siphons of their petri net encoding. Theor. Comput. Sci. 971, 114073 (2023).","journal-title":"Theor. Comput. Sci."},{"key":"36585_CR31","first-page":"874","volume":"25","author":"F Mansoori","year":"2021","unstructured":"Mansoori, F., Rahgozar, M. & Kavousi, K. A pathway analysis approach using petri net. IEEE J. Biomed. Heal Inf. 25, 874\u2013880 (2021).","journal-title":"IEEE J. Biomed. Heal Inf."},{"key":"36585_CR32","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1038\/s41598-022-04849-0","volume":"12","author":"K Gutowska","year":"2022","unstructured":"Gutowska, K. et al. Petri Nets and odes as complementary methods for comprehensive analysis on an example of the ATM\u2013p53\u2013NF-$$\\kappa$$B signaling pathways. Sci. Rep. 12, 1135 (2022).","journal-title":"Sci. Rep."},{"key":"36585_CR33","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.ifacol.2024.07.017","volume":"58","author":"T Moigne","year":"2024","unstructured":"Moigne, T., Le, Mahulea, C. & Faraut, G. Optimizing clinical pathways: A probabilistic time petri net approach. IFAC-PapersOnLine 58, 96\u2013101 (2024).","journal-title":"IFAC-PapersOnLine"},{"key":"36585_CR34","doi-asserted-by":"publisher","unstructured":"Wang, L. & Liu, Z. P. Detecting diagnostic biomarkers of Alzheimer\u2019s disease by integrating gene expression data in six brain regions. Front. Genetics 10 at (2019). https:\/\/doi.org\/10.3389\/fgene.2019.00157","DOI":"10.3389\/fgene.2019.00157"},{"key":"36585_CR35","doi-asserted-by":"crossref","unstructured":"Rybarczyk, A., Formanowicz, D. & Formanowicz, P. Key Therapeutic Targets to Treat Hyperglycemia-Induced Atherosclerosis Analyzed Using a Petri Net-Based Model. Metabolites 13, (2023).","DOI":"10.3390\/metabo13121191"},{"key":"36585_CR36","doi-asserted-by":"publisher","first-page":"659","DOI":"10.3233\/JAD-150440","volume":"49","author":"N Voyle","year":"2015","unstructured":"Voyle, N. et al. B. A pathway based classification method for analyzing gene expression for alzheimer\u2019s disease diagnosis. J. Alzheimer\u2019s Dis. 49, 659\u2013669 (2015).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"36585_CR37","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1186\/s12859-019-3196-4","volume":"20","author":"S Pernice","year":"2019","unstructured":"Pernice, S. et al. A computational approach based on the colored petri net formalism for studying multiple sclerosis. BMC Bioinform. 20, 623 (2019).","journal-title":"BMC Bioinform."},{"key":"36585_CR38","doi-asserted-by":"crossref","unstructured":"Cherdal, S. & Mouline, S. Petri nets for modelling and analysing a complex system related to alzheimer\u2019s disease. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing 309\u2013312 doi:0.1145\/2851613.2851939. (2016).","DOI":"10.1145\/2851613.2851939"},{"key":"36585_CR39","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/pr6080097","volume":"6","author":"S Cherdal","year":"2018","unstructured":"Cherdal, S. & Mouline, S. Modelling and simulation of biochemical processes using petri Nets. Processes 6, 97 (2018).","journal-title":"Processes"},{"key":"36585_CR40","doi-asserted-by":"publisher","first-page":"737","DOI":"10.3233\/JAD-2012-121363","volume":"33","author":"K Lunnon","year":"2013","unstructured":"Lunnon, K. et al. A blood gene expression marker of early alzheimer\u2019s disease. J. Alzheimer\u2019s Dis. 33, 737\u2013753 (2013).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"36585_CR41","doi-asserted-by":"publisher","first-page":"14027","DOI":"10.1038\/s41598-017-13700-w","volume":"7","author":"H Li","year":"2017","unstructured":"Li, H. et al. Identification of molecular alterations in leukocytes from gene expression profiles of peripheral whole blood of alzheimer\u2019s disease. Sci. Rep. 7, 14027 (2017).","journal-title":"Sci. Rep."},{"key":"36585_CR42","doi-asserted-by":"publisher","first-page":"17394","DOI":"10.1038\/s41598-018-35789-3","volume":"8","author":"X Li","year":"2018","unstructured":"Li, X. et al. Systematic analysis and biomarker study for alzheimer\u2019s disease. Sci. Rep. 8, 17394 (2018).","journal-title":"Sci. Rep."},{"key":"36585_CR43","doi-asserted-by":"publisher","first-page":"3485","DOI":"10.1038\/s41598-020-60595-1","volume":"10","author":"T Lee","year":"2020","unstructured":"Lee, T. & Lee, H. Prediction of alzheimer\u2019s disease using blood gene expression data. Sci. Rep. 10, 3485 (2020).","journal-title":"Sci. Rep."},{"key":"36585_CR44","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s41065-022-00252-x","volume":"159","author":"W Ji","year":"2022","unstructured":"Ji, W., An, K., Wang, C. & Wang, S. Bioinformatics analysis of diagnostic biomarkers for alzheimer\u2019s disease in peripheral blood based on sex differences and support vector machine algorithm. Hereditas 159, 38 (2022).","journal-title":"Hereditas"},{"key":"36585_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnagi.2022.881890","volume":"14","author":"Q Qin","year":"2022","unstructured":"Qin, Q. et al. A diagnostic model for alzheimer\u2019s disease based on blood levels of Autophagy-Related genes. Front. Aging Neurosci. 14, 1\u201311 (2022).","journal-title":"Front. Aging Neurosci."},{"key":"36585_CR46","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.3233\/JAD-240301","volume":"100","author":"H Wang","year":"2024","unstructured":"Wang, H. et al. Identification of blood biomarkers related to energy metabolism and construction of diagnostic prediction model based on three independent alzheimer\u2019s disease cohorts. J. Alzheimer\u2019s Dis. 100, 1261\u20131287 (2024).","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"36585_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fgene.2022.968598","volume":"13","author":"Y He","year":"2022","unstructured":"He, Y., Cong, L., He, Q., Feng, N. & Wu, Y. Development and validation of immune-based biomarkers and deep learning models for alzheimer\u2019s disease. Front. Genet. 13, 1\u201315 (2022).","journal-title":"Front. Genet."},{"key":"36585_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnagi.2021.602781","volume":"13","author":"W Yu","year":"2021","unstructured":"Yu, W., Yu, W., Yang, Y. & L\u00fc, Y. Exploring the key genes and identification of potential diagnosis biomarkers in alzheimer\u2019s disease using bioinformatics analysis. Front. Aging Neurosci. 13, 1\u201315 (2021).","journal-title":"Front. Aging Neurosci."},{"key":"36585_CR49","first-page":"1","volume":"13","author":"J Li","year":"2022","unstructured":"Li, J. et al. Identification of diagnostic genes for both alzheimer\u2019s disease and metabolic syndrome by the machine learning algorithm. Front. Immunol. 13, 1\u201313 (2022).","journal-title":"Front. Immunol."},{"key":"36585_CR50","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.3390\/biom13111563","volume":"13","author":"UM Akkaya","year":"2023","unstructured":"Akkaya, U. M. & Kalkan, H. A new approach for multimodal usage of gene expression and its image representation for the detection of alzheimer\u2019s disease. Biomolecules 13, 1563 (2023).","journal-title":"Biomolecules"},{"key":"36585_CR51","doi-asserted-by":"publisher","first-page":"3304","DOI":"10.3390\/biomedicines11123304","volume":"11","author":"MM Abdelwahab","year":"2023","unstructured":"Abdelwahab, M. M., Al-Karawi, K. A. & Semary, H. E. Deep Learning-Based prediction of alzheimer\u2019s disease using microarray gene expression data. Biomedicines 11, 3304 (2023).","journal-title":"Biomedicines"},{"key":"36585_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00117-022-01051-1","volume":"62","author":"KH Nenning","year":"2022","unstructured":"Nenning, K. H. & Langs, G. Machine learning in neuroimaging: from research to clinical practice. Die Radiol. 62, 1\u201310 (2022).","journal-title":"Die Radiol."},{"key":"36585_CR53","doi-asserted-by":"crossref","unstructured":"Kim, K. & Ha, J. G. M. F. L. D. A. Improved prediction of lncRNA- disease association via graph convolutional network. IEEE Access 1 (2025).","DOI":"10.1109\/ACCESS.2025.3568461"},{"key":"36585_CR54","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1109\/TCBB.2022.3191972","volume":"20","author":"J Ha","year":"2023","unstructured":"Ha, J. & Park, S. N. C. M. D. Node2vec-Based neural collaborative filtering for predicting MiRNA-Disease association. IEEE\/ACM Trans. Comput. Biol. Bioinforma. 20, 1257\u20131268 (2023).","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinforma"},{"key":"36585_CR55","doi-asserted-by":"publisher","unstructured":"Ha, J. Graph convolutional network with neural collaborative filtering for predicting miRNA-Disease association. (2025). https:\/\/doi.org\/10.3390\/biomedicines13010136","DOI":"10.3390\/biomedicines13010136"},{"key":"36585_CR56","doi-asserted-by":"publisher","first-page":"110295","DOI":"10.1016\/j.knosys.2023.110295","volume":"263","author":"JSMAP Ha","year":"2023","unstructured":"Ha, J. S. M. A. P. Similarity-based matrix factorization framework for inferring miRNA-disease association. Knowledge-Based Syst. 263, 110295 (2023).","journal-title":"Knowledge-Based Syst."},{"key":"36585_CR57","doi-asserted-by":"publisher","first-page":"3866","DOI":"10.1016\/j.neuron.2022.09.012","volume":"110","author":"NK Dinsdale","year":"2022","unstructured":"Dinsdale, N. K. et al. A. I. L. Challenges for machine learning in clinical translation of big data imaging studies. Neuron 110, 3866\u20133881 (2022).","journal-title":"Neuron"},{"key":"36585_CR58","doi-asserted-by":"publisher","unstructured":"Qayyum Patel, R. A. & Mihailescu, R. C. Reducing Labeling Costs in Alzheimer\u2019s Disease Diagnosis: A Study of Semi-Supervised and Active Learning with 3D Medical Imaging. in International Conference on Modeling, Simulation & Intelligent Computing (MoSICom) 264\u2013269 (IEEE, 2023).https:\/\/doi.org\/10.1109\/MoSICom59118.2023.10458754","DOI":"10.1109\/MoSICom59118.2023.10458754"},{"key":"36585_CR59","doi-asserted-by":"publisher","first-page":"26668","DOI":"10.1038\/s41598-024-77829-1","volume":"14","author":"CY Chang","year":"2024","unstructured":"Chang, C. Y., Slowiejko, D. & Win, N. Prediction and clustering of alzheimer\u2019s disease by race and sex: a multi-head deep-learning approach to analyze irregular and heterogeneous data. Sci. Rep. 14, 26668 (2024).","journal-title":"Sci. Rep."},{"key":"36585_CR60","doi-asserted-by":"publisher","first-page":"D457","DOI":"10.1093\/nar\/gkv1070","volume":"44","author":"M Kanehisa","year":"2016","unstructured":"Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457\u2013D462 (2016).","journal-title":"Nucleic Acids Res."},{"key":"36585_CR61","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","volume":"28","author":"M Kanehisa","year":"2000","unstructured":"Kanehisa, M. & Goto, S. K. E. G. G. Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27\u201330 (2000).","journal-title":"Nucleic Acids Res."},{"key":"36585_CR62","doi-asserted-by":"publisher","first-page":"D672","DOI":"10.1093\/nar\/gkae909","volume":"53","author":"M Kanehisa","year":"2025","unstructured":"Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53, D672\u2013D677 (2025).","journal-title":"Nucleic Acids Res."},{"key":"36585_CR63","doi-asserted-by":"crossref","unstructured":"Gehlot, V. From petri NETS to colored petri NETS: A tutorial introduction to NETS based formalism for modeling and simulation. In: Winter Simulation Conference (WSC) 1519\u20131533 (2019).","DOI":"10.1109\/WSC40007.2019.9004691"},{"key":"36585_CR64","doi-asserted-by":"publisher","unstructured":"Gehlot, V. & Nigro, C. An introduction to systems modeling and simulation with Colored Petri Nets. In: Proceedings of the 2010 Winter Simulation Conference 104\u2013118IEEE, (2010). https:\/\/doi.org\/10.1109\/WSC.2010.5679170","DOI":"10.1109\/WSC.2010.5679170"},{"key":"36585_CR65","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s11277-020-07366-3","volume":"114","author":"KY Tai","year":"2020","unstructured":"Tai, K. Y. et al. Smart fall prediction for elderly care using iPhone and Apple watch. Wirel. Pers. Commun. 114, 347\u2013365 (2020).","journal-title":"Wirel. Pers. Commun."},{"key":"36585_CR66","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1093\/bioinformatics\/btm560","volume":"24","author":"S Hardy","year":"2008","unstructured":"Hardy, S. & Robillard, P. N. Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways. Bioinformatics 24, 209\u2013217 (2008).","journal-title":"Bioinformatics"},{"key":"36585_CR67","doi-asserted-by":"publisher","unstructured":"Pernice, S. et al. Exploiting Stochastic Petri Net formalism to capture the Relapsing Remitting Multiple Sclerosis variability under Daclizumab administration. in IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2168\u20132175 (IEEE, 2019).https:\/\/doi.org\/10.1109\/BIBM47256.2019.8983368","DOI":"10.1109\/BIBM47256.2019.8983368"},{"key":"36585_CR68","doi-asserted-by":"crossref","unstructured":"Ghomri, L. & Alla, H. Modeling and analysis using hybrid petri Nets. 1, 141\u2013153 (2007).","DOI":"10.1016\/j.nahs.2006.04.004"},{"key":"36585_CR69","doi-asserted-by":"publisher","first-page":"e14858","DOI":"10.1016\/j.heliyon.2023.e14858","volume":"9","author":"W Xia","year":"2023","unstructured":"Xia, W., Zhang, R., Zhang, X. & Usman, M. A novel method for diagnosing alzheimer\u2019s disease using deep pyramid CNN based on EEG signals. Heliyon 9, e14858 (2023).","journal-title":"Heliyon"},{"key":"36585_CR70","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1007\/s44174-023-00078-9","volume":"1","author":"KMM Uddin","year":"2023","unstructured":"Uddin, K. M. M., Alam, M. J., Uddin, J. E. A., Aryal, S. & M. A. & A novel approach utilizing machine learning for the early diagnosis of alzheimer\u2019s disease. Biomed. Mater. Devices. 1, 882\u2013898 (2023).","journal-title":"Biomed. Mater. Devices"},{"key":"36585_CR71","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40708-023-00195-7","volume":"10","author":"AD Arya","year":"2023","unstructured":"Arya, A. D. et al. A systematic review on machine learning and deep learning techniques in the effective diagnosis of alzheimer\u2019s disease. Brain Inf. 10, 17 (2023).","journal-title":"Brain Inf."},{"key":"36585_CR72","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco, D. & Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 21, 6 (2020).","journal-title":"BMC Genom."},{"key":"36585_CR73","doi-asserted-by":"publisher","first-page":"93","DOI":"10.3390\/biom14010093","volume":"14","author":"G Garcia-Escobar","year":"2024","unstructured":"Garcia-Escobar, G. et al. Jim\u00e9nez-Balado, J. Blood biomarkers of alzheimer\u2019s disease and cognition: A literature review. Biomolecules 14, 93 (2024).","journal-title":"Biomolecules"},{"key":"36585_CR74","doi-asserted-by":"publisher","first-page":"875895","DOI":"10.3389\/fdgth.2022.875895","volume":"4","author":"SL Oh","year":"2022","unstructured":"Oh, S. L. et al. Alzheimer\u2019s disease blood biomarkers associated with neuroinflammation as therapeutic targets for early personalized intervention. Front. Digit. Heal. 4, 875895 (2022).","journal-title":"Front. Digit. Heal"},{"key":"36585_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfma.2024.01.018","author":"YW Cheng","year":"2024","unstructured":"Cheng, Y. W. et al. Application of blood-based biomarkers of alzheimer\u2019s disease in clinical practice: recommendations from Taiwan dementia society. J. Formos. Med. Assoc. https:\/\/doi.org\/10.1016\/j.jfma.2024.01.018 (2024).","journal-title":"J. Formos. Med. Assoc."},{"key":"36585_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/cells11081367","volume":"11","author":"A Varesi","year":"2022","unstructured":"Varesi, A. et al. Blood-Based biomarkers for alzheimer\u2019s disease diagnosis and progression: an overview. Cells 11, 1\u201342 (2022).","journal-title":"Cells"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36585-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36585-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36585-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T17:11:31Z","timestamp":1771434691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-36585-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,28]]},"references-count":76,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["36585"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-36585-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,28]]},"assertion":[{"value":"30 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6457"}}