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In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene\u2013gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n\u2009=\u2009364\u00a0733), exposure to promethazine, one of the candidate drugs, was associated with a 57%\u201362% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P\u2009&amp;lt;\u2009.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.<\/jats:p>","DOI":"10.1093\/bib\/bbaf623","type":"journal-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:03:28Z","timestamp":1762607008000},"source":"Crossref","is-referenced-by-count":9,"title":["Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer\u2019s disease"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1398-7540","authenticated-orcid":false,"given":"Mohammadsadeq","family":"Mottaqi","sequence":"first","affiliation":[{"name":"Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York , 365 Fifth Avenue, New York, NY 10016 ,","place":["United 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Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University , 413 E 69th St, New York, NY 10065 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"2025112810210602300_ref1","doi-asserted-by":"crossref","first-page":"115522","DOI":"10.1016\/j.bcp.2023.115522","article-title":"Alzheimer\u2019s disease: insights and new prospects in disease pathophysiology, biomarkers and disease-modifying drugs","volume":"211","author":"Monteiro","year":"2023","journal-title":"Biochem Pharmacol"},{"key":"2025112810210602300_ref2","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/s41392-024-01911-3","article-title":"Recent advances in Alzheimer\u2019s disease: mechanisms, clinical trials and new drug development strategies","volume":"9","author":"Zhang","year":"2024","journal-title":"Signal Transduct Target 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