{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003406105,0.0002258997,0.0001408596,0.0001384335,0.0002774529,0.00004098176,0.0003520513,0.00003401101,0.00005162572],"category_scores_gemma":[0.0001156498,0.0002641139,0.00009990878,0.0001400393,0.00004150967,0.00002205997,0.0005040328,0.0002608296,0.00001703228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007594675,"about_ca_system_score_gemma":0.0001338882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001085574,"about_ca_topic_score_gemma":0.00003252262,"domain_scores_codex":[0.998087,0.0002498179,0.0003013647,0.0005867113,0.0002941082,0.0004810166],"domain_scores_gemma":[0.9992085,0.000007490084,0.0001036863,0.0003629937,0.00002823442,0.00028917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001706972,0.0002600594,0.5652279,0.0001406475,0.00005078307,0.0004919609,0.0002663851,0.4078036,0.02169,0.0002480218,0.000163227,0.001950371],"study_design_scores_gemma":[0.003426677,0.0003453862,0.9122659,0.00005182662,0.00007740897,0.00007320139,0.001078642,0.02783174,0.0008319074,0.0003997659,0.05231195,0.001305581],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929497,0.002232795,0.003572992,0.00003152595,0.0002011284,0.0004137022,0.00008299978,0.00003995283,0.0004752596],"genre_scores_gemma":[0.9967467,0.000064575,0.001694323,0.0002116657,0.000125936,0.0004143898,0.0002490651,0.00004602067,0.0004473172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3799719,"threshold_uncertainty_score":0.9999811,"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."}}