{"id":"W3199639770","doi":"10.1016/j.ajhg.2021.08.012","title":"Improved pathogenicity prediction for rare human missense variants","year":2021,"lang":"en","type":"article","venue":"The American Journal of Human Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":183,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Lunenfeld-Tanenbaum Research Institute; Ontario Institute for Cancer Research; University of Toronto","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research","keywords":"Missense mutation; Pathogenicity; Inference; Computer science; Artificial intelligence; Machine learning; Annotation; Exploit; Limit (mathematics); Feature (linguistics); Personalized medicine; Computational biology; Data mining; Genetics; Biology; Mutation; Mathematics; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005192931,0.0009262437,0.001046955,0.002381253,0.00064241,0.001521577,0.00107574,0.001099495,0.001386341],"category_scores_gemma":[0.01625228,0.0004222951,0.001072712,0.0008804317,0.0006165613,0.001526604,0.001919525,0.001362444,0.0004482062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006676039,"about_ca_system_score_gemma":0.001623711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004825768,"about_ca_topic_score_gemma":0.00668406,"domain_scores_codex":[0.9984401,0.0005804866,0.00009421251,0.0003919499,0.0003741771,0.0001189861],"domain_scores_gemma":[0.9918485,0.005916153,0.0005049261,0.0007251591,0.00078576,0.0002195141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001745068,0.0003881338,0.0953626,0.0004520459,0.0007021383,0.001201248,0.0003622504,0.528233,0.05192739,0.008571189,0.009151598,0.3019032],"study_design_scores_gemma":[0.00005995113,0.0001321735,0.005467136,0.00002782647,0.00007856041,0.0002847901,0.00002829021,0.9791185,0.008035311,0.006000868,0.0007269229,0.00003961017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7374905,0.001450775,0.2466883,0.001157926,0.000164504,0.0001069292,0.001565101,0.007679097,0.003696838],"genre_scores_gemma":[0.9040901,0.0002204243,0.09275474,0.000236249,0.00007216198,0.0000332165,0.001674296,0.0001797394,0.0007390341],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005192931,"threshold_uncertainty_score":0.0274632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106866392971394,"score_gpt":0.2631910648771735,"score_spread":0.2521224009474595,"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."}}