{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:50:44Z","timestamp":1780527044151,"version":"3.54.1"},"reference-count":67,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ERC ASTRA_855923"},{"name":"EIC Pathfinder IVBM4PAP_101098989"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases like multiple sclerosis (MS) and Alzheimer\u2019s disease (AD). We tested logistic regression (LR), ensemble tree methods, and deep learning models for this purpose. LR displayed remarkable stability across various subsets of data, outshining deep learning approaches, which showed greater variability in performance. Additionally, ML methods demonstrated an ability to maintain optimal performance despite correlated genomic features due to linkage disequilibrium. When comparing the performance of polygenic risk score (PRS) with ML methods, PRS consistently performed at an average level. By employing explainability tools in the ML models of MS, we found that the results confirmed the polygenicity of this disease. The highest-prioritized genomic variants in MS were identified as expression or splicing quantitative trait loci located in non-coding regions within or near genes associated with the immune response, with a prevalence of human leukocyte antigen (HLA) gene annotations. Our findings shed light on both the potential and the challenges of employing ML to capture complex genomic patterns, paving the way for improved predictive models.<\/jats:p>","DOI":"10.3390\/ijms26052085","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T03:37:57Z","timestamp":1740627477000},"page":"2085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer\u2019s Disease Using Genomic Data"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8647-9232","authenticated-orcid":false,"given":"Magdalena","family":"Arnal Segura","sequence":"first","affiliation":[{"name":"Centre for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy"},{"name":"Department of Biology \u2018Charles Darwin\u2019, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"Bini","sequence":"additional","affiliation":[{"name":"Centre for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy"},{"name":"Department of Physics, University of Genova, Via Dodecaneso 33, 16146 Genova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0491-4507","authenticated-orcid":false,"given":"Anastasia","family":"Krithara","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telecommunications, National Center for Scientific Research \u201cDemokritos\u201d, 15341 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Paliouras","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telecommunications, National Center for Scientific Research \u201cDemokritos\u201d, 15341 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gian Gaetano","family":"Tartaglia","sequence":"additional","affiliation":[{"name":"Centre for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy"},{"name":"Department of Biology \u2018Charles Darwin\u2019, Sapienza University of Rome, P.le A. 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