{"id":"W4401822209","doi":"10.1016/j.xhgg.2024.100344","title":"The performance of AlphaMissense to identify genes influencing disease","year":2024,"lang":"en","type":"article","venue":"Human Genetics and Genomics Advances","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University; Jewish General Hospital","funders":"Medical Research Council; Fonds de Recherche du Québec - Santé; National Institutes of Health; Fondation de l'Hôpital général juif; Medical Research Council Canada; Public Health Agency; European Commission; TD Bank; King's College London; Jewish General Hospital; National Institute for Health and Care Research; Wellcome Trust; Canada Foundation for Innovation; Génome Québec; Public Health Agency of Canada; McGill University; Canadian Institutes of Health Research; Compute Canada; Japan Society for the Promotion of Science; Cancer Research UK","keywords":"Gene; Genetics; Biology; Disease; Computational biology; Evolutionary biology; Medicine; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001313309,0.0001513988,0.0001059718,0.00004563301,0.0002703592,0.000117473,0.0001991847,0.00003692213,0.000003644137],"category_scores_gemma":[0.00001268462,0.0001187324,0.00006972862,0.00006407349,0.0001246805,0.000005052804,0.000182722,0.00004384543,0.000004250096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009351651,"about_ca_system_score_gemma":0.00008010064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003072355,"about_ca_topic_score_gemma":0.00002374088,"domain_scores_codex":[0.999099,0.00001721578,0.0002416553,0.0003302397,0.00009645952,0.0002154207],"domain_scores_gemma":[0.999395,0.00001694781,0.00004855405,0.0003075256,0.00006777127,0.0001642285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008059389,0.00002293903,0.004391577,0.0002146215,0.00007786577,0.00001145451,0.000152629,0.003009147,0.9497458,0.000544382,0.0003203306,0.04142861],"study_design_scores_gemma":[0.0003374453,0.0006068852,0.06651097,0.0001222647,0.0001283187,0.00002828765,0.0003937253,0.002313182,0.1758115,0.001553733,0.7514971,0.0006965762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8720917,0.1273216,0.00005046602,0.00006279552,0.0001878837,0.0001535701,0.00004357177,0.000007286985,0.00008111067],"genre_scores_gemma":[0.968691,0.03040247,0.0002626819,0.00008574344,0.0002069292,0.0000169815,0.00002499432,0.00002651887,0.0002826829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7739344,"threshold_uncertainty_score":0.4841768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007771629180966091,"score_gpt":0.2840540544750202,"score_spread":0.2762824252940542,"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."}}