{"id":"W2616194201","doi":"10.1002/humu.23257","title":"CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants","year":2017,"lang":"en","type":"article","venue":"Human Mutation","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Human Genome Research Institute; National Institutes of Health; Hospital for Sick Children","keywords":"Biology; Sanger sequencing; Prioritization; Computational biology; Genome; Genetics; Disease; Gene; Whole genome sequencing; Phenotype; Genomics; DNA sequencing; Medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0104029,0.00174075,0.001541545,0.005381353,0.001397783,0.003151231,0.002286876,0.001716067,0.00899398],"category_scores_gemma":[0.02068826,0.001217191,0.001575496,0.002971308,0.0005327858,0.001452534,0.003802509,0.003023731,0.004741092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001407534,"about_ca_system_score_gemma":0.005506952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009939984,"about_ca_topic_score_gemma":0.02003699,"domain_scores_codex":[0.9964703,0.0008521266,0.0004271722,0.0008714877,0.001108278,0.0002706125],"domain_scores_gemma":[0.9917665,0.003648564,0.0007454343,0.0009271468,0.001957918,0.0009544416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002622376,0.0003296117,0.04038642,0.001715419,0.0007847521,0.002679458,0.001790343,0.008258218,0.04439606,0.007260273,0.5240083,0.3657688],"study_design_scores_gemma":[0.002317953,0.0009823184,0.09111905,0.0008969344,0.0006402662,0.006892066,0.001364667,0.1468752,0.06510793,0.05117784,0.6317663,0.0008595953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07673553,0.0079057,0.5222754,0.02363181,0.00117241,0.006627178,0.2261961,0.1185379,0.01691798],"genre_scores_gemma":[0.07575146,0.001918636,0.7648988,0.003439777,0.0003910556,0.00259694,0.1406272,0.006694573,0.003681541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0104029,"threshold_uncertainty_score":0.05501646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08097337414239415,"score_gpt":0.3953557982809747,"score_spread":0.3143824241385805,"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."}}