{"id":"W3215725522","doi":"10.1016/j.gastha.2021.11.002","title":"A Machine Learning Approach to Identifying Causal Monogenic Variants in Inflammatory Bowel Disease","year":2022,"lang":"en","type":"article","venue":"Gastro Hep Advances","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; University of Toronto; Hospital for Sick Children; Queen's University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Hospital for Sick Children; University of Toronto; Canada Research Chairs; Canadian Association of Gastroenterology; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Inflammatory bowel disease; Exome sequencing; Exome; Prioritization; Disease; Pipeline (software); Computational biology; Machine learning; Medicine; Bioinformatics; Biology; Gene; Computer science; Genetics; Mutation; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003899597,0.001138864,0.001218523,0.003348812,0.0008180521,0.00165393,0.001668858,0.001365228,0.001438906],"category_scores_gemma":[0.008708742,0.0003984426,0.0009872476,0.001582484,0.0006604865,0.0007099363,0.0008884993,0.001879125,0.0005261665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147968,"about_ca_system_score_gemma":0.001455972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004007782,"about_ca_topic_score_gemma":0.004311731,"domain_scores_codex":[0.9981548,0.0008090308,0.0001649915,0.0004513817,0.0003107584,0.000108964],"domain_scores_gemma":[0.9954243,0.003422285,0.0003049203,0.0002309078,0.0005147278,0.0001027085],"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":[0.000409694,0.0005512365,0.0486981,0.0003529079,0.0006522826,0.00062225,0.0002498748,0.3125305,0.005404145,0.01151467,0.00595687,0.6130575],"study_design_scores_gemma":[0.00002975335,0.00008579354,0.003943317,0.00004346699,0.00005782009,0.0002004952,0.00003596961,0.9752263,0.001038396,0.01793113,0.00138903,0.00001850099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0399473,0.002056969,0.9520764,0.001264448,0.0001020443,0.0002525513,0.0005467499,0.001503656,0.002249897],"genre_scores_gemma":[0.4428996,0.0009421731,0.5517755,0.000784081,0.0002685323,0.0005208546,0.001017567,0.00009583522,0.001695802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004007782,"threshold_uncertainty_score":0.02062333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00835024273727361,"score_gpt":0.2399005048423083,"score_spread":0.2315502621050347,"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."}}