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This autoimmune attack leads to the destruction of myelin, resulting in the neurological symptoms characteristic of MS. Identifying myelin autoantigenic peptides is crucial for understanding the pathogenesis of MS and developing targeted therapies. Traditional approaches often struggle with the complexity and heterogeneity of biological data, making it challenging to achieve accurate predictions in a cost\u2010effective manner. Alternatively, computational approaches that utilize sequence information can aid in the biological elucidation of peptides. In this study, we present a novel propensity score\u2010based approach, termed PSR\u2010MAPMS, to predict and characterize T cell\u2010specific myelin autoantigenic peptides in MS (MAPMSs). To the extent of our knowledge, PSR\u2010MAPMS is the first machine learning (ML)\u2010based approach that can predict and analyze MAPMSs based solely on sequence information. In PSR\u2010MAPMS, we generated multiple aspects of propensity scores for MAPMSs. Important propensity scores were then chosen and applied to create the final hybrid model using an ensemble learning strategy. Extensive experiments results showed that PSR\u2010MAPMS surpasses several conventional ML\u2010based classifiers for MAPMS prediction in both cross\u2010validation and independent tests. In the independent test results, the accuracy, MCC, and F1 scores of PSR\u2010MAPMS were within the ranges of 0.899\u20130.949, 0.800\u20130.899, and 0.903\u2013949, respectively. Moreover, our estimated propensity scores can identify crucial biochemical and physicochemical properties of MAPMSs, providing valuable revelations of the fundamental biological mechanisms, which facilitates the development of more effective and personalized treatments for MS. In addition, we created a simple\u2010to\u2010navigate web server for PSR\u2010MAPMS, which is publicly accessible at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/pmlabqsar.pythonanywhere.com\/PSR-MAPMS\">https:\/\/pmlabqsar.pythonanywhere.com\/PSR-MAPMS<\/jats:ext-link>.<\/jats:p>","DOI":"10.1002\/pro.70010","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T10:48:52Z","timestamp":1752749332000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["<scp>PSR<\/scp>\u2010<scp>MAPMS<\/scp>: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis using multi\u2010source propensity scores"],"prefix":"10.1002","volume":"34","author":[{"given":"Phasit","family":"Charoenkwan","sequence":"first","affiliation":[{"name":"Modern Management and Information Technology, College of Arts, Media and Technology 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