{"id":"W2088338354","doi":"10.1126/science.1254806","title":"The human splicing code reveals new insights into the genetic determinants of disease","year":2014,"lang":"en","type":"article","venue":"Science","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1306,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto; Canadian Institute for Advanced Research","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; University of Toronto; Autism Speaks; National Cancer Institute; McLaughlin Centre, University of Toronto; Ontario Genomics; National Institutes of Health; Hospital for Sick Children; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics Institute","keywords":"RNA splicing; Genetics; Missense mutation; Biology; Gene; Genome; Phenotype; Intron; Mutation; Computational biology; Disease; RNA; 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":[0.0005652328,0.0003131432,0.0004356484,0.0009862441,0.0001930712,0.0005602591,0.0002030938,0.0004285419,0.0008455362],"category_scores_gemma":[0.001175421,0.0001613401,0.0002445066,0.0008368272,0.0003992525,0.0004088306,0.0003074973,0.0004728218,0.0002958008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849898,"about_ca_system_score_gemma":0.0001962835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003168943,"about_ca_topic_score_gemma":0.0007751077,"domain_scores_codex":[0.9997557,0.00005122053,0.0000222738,0.00009177221,0.00006414449,0.00001487007],"domain_scores_gemma":[0.9993638,0.000329715,0.0001236069,0.0001037428,0.00004053289,0.00003858155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007605926,0.00006280005,0.08790962,0.0002830222,0.0002321862,0.002339132,0.0003869901,0.009621764,0.7558377,0.0179989,0.001196633,0.1233707],"study_design_scores_gemma":[0.0001389994,0.0006763054,0.3015806,0.0002175334,0.0005771981,0.0141237,0.0004021547,0.1406287,0.2847264,0.1909307,0.06581105,0.000186768],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8835754,0.007401931,0.1007406,0.001137712,0.0001256357,0.00002114151,0.002336149,0.0005444094,0.004117071],"genre_scores_gemma":[0.9613645,0.002200705,0.03414274,0.0002982293,0.0001181862,0.00001253802,0.00105371,0.00007744075,0.000731971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009862441,"threshold_uncertainty_score":0.002989233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01261340573962562,"score_gpt":0.2726524747851433,"score_spread":0.2600390690455177,"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."}}