{"id":"W2891147136","doi":"10.1186/s12863-018-0645-4","title":"Causal modeling in a multi-omic setting: insights from GAW20","year":2018,"lang":"en","type":"article","venue":"BMC Genetics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; Instituto de Salud Carlos III; National Institutes of Health; Fundació Privada Daniel Bravo Andreu","keywords":"Mendelian randomization; Causal inference; Context (archaeology); Inference; Computational biology; Structural equation modeling; Computer science; Causality (physics); Genomics; Bioinformatics; Biology; Machine learning; Artificial intelligence; Econometrics; Mathematics; Genetics; Genome; Gene","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":[0.01914324,0.001669758,0.002178325,0.004104197,0.001056583,0.002716146,0.002326062,0.001902103,0.001803415],"category_scores_gemma":[0.06268694,0.0008383747,0.003882473,0.004012593,0.00256344,0.002164572,0.003233909,0.00406671,0.0002881528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001905589,"about_ca_system_score_gemma":0.003790634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01793987,"about_ca_topic_score_gemma":0.01593356,"domain_scores_codex":[0.9895225,0.007392337,0.0003564771,0.001492079,0.0009918574,0.0002447676],"domain_scores_gemma":[0.9553393,0.03782166,0.001856855,0.003248045,0.001176152,0.0005579125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001338199,0.0001028904,0.02606178,0.0004826864,0.002136606,0.001369964,0.0007506445,0.1878547,0.002000338,0.6493934,0.004841389,0.1248717],"study_design_scores_gemma":[0.00003956512,0.00004430487,0.004742545,0.0001270883,0.0002993571,0.0003585918,0.00006735628,0.2993688,0.0002718947,0.6859372,0.008675797,0.00006748989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02158281,0.002849908,0.9686585,0.004008739,0.0001189703,0.00006276588,0.000641608,0.0003932746,0.001683529],"genre_scores_gemma":[0.3735788,0.005294609,0.6133242,0.002726324,0.0006289969,0.0004240632,0.001264808,0.0004290576,0.00232916],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01914324,"threshold_uncertainty_score":0.1012403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05209731537627268,"score_gpt":0.2985948598561878,"score_spread":0.2464975444799151,"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."}}