{"id":"W4383216982","doi":"10.1093/nargab/lqad065","title":"GCPBayes pipeline: a tool for exploring pleiotropy at the gene level","year":2023,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Ligue Contre le Cancer; Ovarian Cancer Research Fund; National Institutes of Health; Cancer Research UK; Government of Canada; Institut National de la Santé et de la Recherche Médicale; Fondation du cancer du sein du Québec; Canadian Institutes of Health Research; Gray Foundation; Genome Canada; European Commission; Breast Cancer Research Foundation","keywords":"Pleiotropy; Pipeline (software); Context (archaeology); Computer science; Set (abstract data type); Genome-wide association study; Computational biology; Gene; Genome; Data mining; Biology; Phenotype; Genetics; Single-nucleotide polymorphism","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002381184,0.0001213036,0.0001068298,0.00003449927,0.0003478623,0.00005965881,0.0001390604,0.0000756571,0.000006066342],"category_scores_gemma":[0.00004319716,0.00008657426,0.00007273607,0.00005806001,0.00004992407,0.000004592513,0.0002393887,0.00003880497,0.00003104081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009303641,"about_ca_system_score_gemma":0.00003147017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004562996,"about_ca_topic_score_gemma":0.000007933807,"domain_scores_codex":[0.9993419,0.000006997463,0.000198424,0.0001329799,0.00008115605,0.0002385468],"domain_scores_gemma":[0.9996243,0.00002569743,0.00006810124,0.0001842095,0.00004761326,0.00005012709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006437285,0.00007560135,0.004620864,0.0005576327,0.0003442907,0.000006825036,0.003661058,0.001274553,0.4938788,0.0009823637,0.3598231,0.1341312],"study_design_scores_gemma":[0.001292288,0.0004122677,0.002803725,0.0000199914,0.00006215376,0.00005070993,0.001927355,0.02577144,0.06501906,0.0001755177,0.9019638,0.0005016461],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942534,0.000283251,0.003799117,0.0004172391,0.0001751085,0.000234795,0.0005446959,0.000018922,0.0002734954],"genre_scores_gemma":[0.9223756,0.01670451,0.03385774,0.002317697,0.001289864,0.0001305475,0.002369014,0.00006186706,0.02089315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5421407,"threshold_uncertainty_score":0.3530396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06137306226799964,"score_gpt":0.2430030234408659,"score_spread":0.1816299611728662,"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."}}