{"id":"W4412534814","doi":"10.1038/s41467-025-61650-z","title":"Machine learning in Alzheimer’s disease genetics","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute; University Health Network; University of Toronto; Artificial Intelligence in Medicine (Canada)","funders":"Medical Research Council; Bentham-Moxon Trust; Ministero della Salute; Fonds De La Recherche Scientifique - FNRS; European Commission; ZonMw; UK Dementia Research Institute; Université de Lille","keywords":"Genome-wide association study; Multifactor dimensionality reduction; Machine learning; Artificial intelligence; Computational biology; Computer science; Genetic association; Locus (genetics); Boosting (machine learning); Precision medicine; Replicate; Linkage disequilibrium; Biology; Genetics; Single-nucleotide polymorphism; Genotype; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00563151,0.0004817066,0.0009681318,0.001238419,0.0003165118,0.001551899,0.000535553,0.001043559,0.0007073518],"category_scores_gemma":[0.01246206,0.0002616469,0.0004703698,0.001375535,0.001355258,0.0008760564,0.0008327565,0.002098215,0.000237395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008363266,"about_ca_system_score_gemma":0.001069734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001802871,"about_ca_topic_score_gemma":0.001590856,"domain_scores_codex":[0.9978637,0.001573975,0.00007109787,0.0001745596,0.0002510531,0.00006558856],"domain_scores_gemma":[0.9915862,0.00720998,0.0003370447,0.0003970515,0.0003631815,0.0001066755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001423868,0.0001853609,0.04183963,0.0007427321,0.0007501096,0.0002665044,0.0002980597,0.3653774,0.001902401,0.1600906,0.01003794,0.4183668],"study_design_scores_gemma":[0.00004161249,0.0001116564,0.011341,0.000228457,0.0000471991,0.0001322563,0.00008181121,0.5808825,0.0008112073,0.3958268,0.01045294,0.00004254587],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1291917,0.09020004,0.7292171,0.03449534,0.001473681,0.0001535862,0.0006690391,0.0007711653,0.01382842],"genre_scores_gemma":[0.8051838,0.02030952,0.1681091,0.001820223,0.001442237,0.0001975002,0.0003803772,0.00005377016,0.00250343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00563151,"threshold_uncertainty_score":0.02978259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02004327286411536,"score_gpt":0.3314523839446207,"score_spread":0.3114091110805054,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}