{"id":"W3113012371","doi":"10.1101/2020.12.04.408336","title":"GeneTerpret: a customizable multilayer approach to genomic variant prioritization and interpretation","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University Health Network; SickKids Foundation; University of Toronto; Hospital for Sick Children; York University; Ted Rogers Centre for Heart Research","funders":"Hospital for Sick Children; Wellcome Trust","keywords":"Bottleneck; Computer science; Prioritization; Genome; Interpretation (philosophy); Categorization; Computational biology; Data mining; Artificial intelligence; Genetics; Gene; Biology; Programming language; Engineering","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":[0.006277008,0.001980725,0.001333558,0.00407327,0.0007886209,0.005222915,0.002947274,0.00155612,0.01898009],"category_scores_gemma":[0.01366548,0.001458574,0.002558118,0.001802179,0.0009032979,0.002466473,0.00582007,0.0021364,0.006342317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001089503,"about_ca_system_score_gemma":0.001743127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003769295,"about_ca_topic_score_gemma":0.006304998,"domain_scores_codex":[0.9970863,0.0006461359,0.000332995,0.0009163098,0.0008427333,0.0001754463],"domain_scores_gemma":[0.9942672,0.00292788,0.000429266,0.001309626,0.0007106827,0.0003553663],"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":[0.004836249,0.0004294638,0.02237939,0.001565988,0.001628764,0.003533458,0.001694152,0.04710699,0.06746967,0.03454798,0.2152167,0.5995914],"study_design_scores_gemma":[0.000824793,0.0003414087,0.009879098,0.0004385188,0.0004672759,0.002643209,0.0003672773,0.636182,0.0843876,0.1147935,0.1490598,0.0006155408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006863346,0.000291977,0.7818822,0.0005298643,0.0001860007,0.000352874,0.005188674,0.2022149,0.00249014],"genre_scores_gemma":[0.09527233,0.0003685151,0.863936,0.000932903,0.0001750382,0.0005484887,0.01120428,0.02135725,0.006205108],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01898009,"threshold_uncertainty_score":0.06349474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008676033791727829,"score_gpt":0.2074458512373252,"score_spread":0.1987698174455974,"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."}}