{"id":"W2752299790","doi":"10.1038/ng.3947","title":"A functional genomics predictive network model identifies regulators of inflammatory bowel disease","year":2017,"lang":"en","type":"article","venue":"Nature Genetics","topic":"Inflammatory Bowel Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":271,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; National Institute of Mental Health; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institute on Aging; National Institutes of Health","keywords":"Inflammatory bowel disease; Genome-wide association study; Biology; Computational biology; Disease; Gene regulatory network; Genomics; Systems biology; Genetic association; Genome; Genetics; Gene; Medicine; Single-nucleotide polymorphism; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007593655,0.0006687801,0.0005155234,0.0009707618,0.0002995912,0.0008590753,0.0009792355,0.000573027,0.003519319],"category_scores_gemma":[0.002356863,0.0002335764,0.0006307915,0.0006623107,0.0004168498,0.0008044779,0.0004614629,0.0006318833,0.0003107319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008878579,"about_ca_system_score_gemma":0.00093373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006533177,"about_ca_topic_score_gemma":0.006893669,"domain_scores_codex":[0.999684,0.0001211684,0.000007278853,0.0001079669,0.00004377628,0.00003595307],"domain_scores_gemma":[0.9991676,0.000562196,0.00008810443,0.00004203838,0.00009005745,0.00005000673],"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":[0.0004265366,0.0002274365,0.01459571,0.00009994898,0.0001995198,0.0003718525,0.00006894194,0.8979866,0.006162287,0.04656951,0.002560399,0.03073128],"study_design_scores_gemma":[0.00001816514,0.00002803225,0.0014372,0.000004568974,0.0000444588,0.00003347871,0.000009184327,0.9793021,0.000318263,0.01845536,0.0003447945,0.000004269838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4136255,0.0007006453,0.5633472,0.003470552,0.00008958445,0.0001422703,0.004485508,0.001204972,0.01293378],"genre_scores_gemma":[0.972585,0.00025526,0.02293061,0.0001423492,0.00004031892,0.00009538078,0.001234967,0.00003717354,0.002678802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006533177,"threshold_uncertainty_score":0.0129903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005918687323338161,"score_gpt":0.2263174284773962,"score_spread":0.220398741154058,"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."}}