{"id":"W3001747143","doi":"10.1101/2020.01.24.918672","title":"Bayesian copy number detection and association in large-scale studies","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"","keywords":"Copy-number variation; Bayesian probability; Computational biology; Biology; Genome-wide association study; Germline; Genetics; Genome; Computer science; Gene; Artificial intelligence; Genotype; Single-nucleotide polymorphism","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":[0.05882416,0.0006747133,0.001186563,0.002173319,0.0007690654,0.002650561,0.002468287,0.001969861,0.00133722],"category_scores_gemma":[0.1336692,0.0009913188,0.001388118,0.00241372,0.002385162,0.001740527,0.003009262,0.001873508,0.0001691826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249174,"about_ca_system_score_gemma":0.001295318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004967182,"about_ca_topic_score_gemma":0.005661781,"domain_scores_codex":[0.9755036,0.0186537,0.0007492634,0.003003365,0.001855393,0.0002346963],"domain_scores_gemma":[0.8383702,0.1442443,0.008014976,0.006882906,0.001850452,0.0006371177],"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":[0.0008777473,0.0001980523,0.3129769,0.001013703,0.004484504,0.001307486,0.001022904,0.387543,0.01626892,0.1092861,0.002824922,0.1621957],"study_design_scores_gemma":[0.0001713347,0.000133065,0.05551594,0.0001230493,0.0005391442,0.0004411847,0.0001145725,0.7404505,0.003811888,0.1958583,0.002757044,0.00008398655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09455957,0.001332714,0.9012588,0.0007406873,0.00003602747,0.0001532042,0.0004677231,0.0004443999,0.001006908],"genre_scores_gemma":[0.7443853,0.0004911298,0.253105,0.0002875826,0.00008965565,0.0003342488,0.0006762329,0.0001019745,0.0005289555],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05882416,"threshold_uncertainty_score":0.3110957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009169159131017525,"score_gpt":0.2247465457905993,"score_spread":0.2155773866595818,"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."}}