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Traditional models for estimating AD onset cannot capture nonlinear interactions (epistasis) among the numerous genetic variables that contribute to AD risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a feedforward neural network (FFN)\u2013Weibull survival model to predict AD onset using large-scale single-nucleotide polymorphism (SNP) data. We integrated an XAI technique, Shapley additive explanations (SHAP), to address the black-box nature of deep learning, interpret model predictions, and quantify the contribution of each genetic factor to AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The FFN model achieved a mean concordance index of 0.647, demonstrating an approximately 3.6% improvement over the traditional linear baseline (0.625). The FFN-SHAP model validated established findings, identifying APOE E4 as a primary AD risk factor. APOE E2 strongly protected against AD. Metabolic-disorder-related SNPs had conflicting effects, suggesting gene\u2013environment interactions influence AD onset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>By effectively bypassing the combinatorial explosion of interaction terms, the predictive power of an FFN combined with XAI provides a robust methodological tool for identifying the genetic basis of complex diseases, even in cohorts with limited sample sizes. Our model generated novel testable hypotheses regarding the intricate roles of gene\u2013gene and gene\u2013environment interactions in AD pathogenesis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag213","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:27:04Z","timestamp":1777289224000},"source":"Crossref","is-referenced-by-count":0,"title":["Interpretable deep survival analysis of Alzheimer\u2019s disease via metabolic genetic variants"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-5921","authenticated-orcid":false,"given":"Sungwoo","family":"Goo","sequence":"first","affiliation":[{"name":"College of Pharmacy, Chungnam National University , Daejeon, 34134,","place":["Republic of Korea"]},{"name":"Department of Bio-AI convergence, Chungnam National University , Daejeon, 34134,","place":["Republic of 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