{"id":"W3171310600","doi":"10.3389/fgene.2021.647436","title":"A Machine Learning Method to Identify Genetic Variants Potentially Associated With Alzheimer’s Disease","year":2021,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institutes of Health; Karl-Franzens-Universität Graz; Texas Alzheimer's Research and Care Consortium; Österreichische Forschungsförderungsgesellschaft; National Institute on Aging; Medizinische Universität Graz; Oesterreichische Nationalbank; Case Western Reserve University; University of Toronto; European Commission; Austrian Science Fund; National Institute on Deafness and Other Communication Disorders; University of Miami; EU Joint Programme – Neurodegenerative Disease Research; National Human Genome Research Institute; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Vanderbilt University","keywords":"Overfitting; Neuropathology; Single-nucleotide polymorphism; Disease; Exome; Genetic association; Computational biology; Bioinformatics; Alzheimer's disease; Biology; Exome sequencing; Medicine; Genetics; Artificial intelligence; Gene; Genotype; Artificial neural network; Computer science; Internal medicine; Mutation","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003183001,0.0005749654,0.0005853259,0.001847694,0.0004467253,0.0007344696,0.0005703208,0.0007162621,0.001627667],"category_scores_gemma":[0.01295345,0.0002587257,0.0007981593,0.001279579,0.0006148812,0.0007343461,0.0006935628,0.001031251,0.0004423209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004806728,"about_ca_system_score_gemma":0.0006118372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001996019,"about_ca_topic_score_gemma":0.002151602,"domain_scores_codex":[0.9989769,0.0004800396,0.00005711388,0.0002671637,0.0001842309,0.00003452651],"domain_scores_gemma":[0.9955428,0.003511893,0.0003367068,0.0003017164,0.0002531791,0.00005374874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004174335,0.0002764309,0.1033466,0.0004400739,0.001508618,0.0002795816,0.0002073375,0.3164091,0.009386127,0.03789653,0.006743496,0.5230887],"study_design_scores_gemma":[0.0000528788,0.00009520762,0.01499634,0.00007370728,0.0001272253,0.0003386681,0.00003315163,0.9090098,0.002368899,0.06837685,0.004488498,0.00003876233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04706809,0.00113954,0.946681,0.001015656,0.0001346948,0.00007631986,0.0007023313,0.0005186502,0.002663883],"genre_scores_gemma":[0.6115328,0.0009847702,0.3820757,0.0006593587,0.0002666016,0.0003179843,0.001190461,0.00007875896,0.002893523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003183001,"threshold_uncertainty_score":0.01683354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550427350432422,"score_gpt":0.2937245734043784,"score_spread":0.2782202999000541,"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."}}