{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003380366,0.003477603,0.002103804,0.003767779,0.001351751,0.002071918,0.002827182,0.00137624,0.07607163],"category_scores_gemma":[0.007893314,0.00185996,0.003929852,0.002139788,0.0006681141,0.002280156,0.003303849,0.003267267,0.02436588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006891398,"about_ca_system_score_gemma":0.002320842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003968278,"about_ca_topic_score_gemma":0.005462916,"domain_scores_codex":[0.9990521,0.0001941815,0.00008252297,0.0003569605,0.0002176367,0.00009667715],"domain_scores_gemma":[0.9974781,0.001647578,0.0001575505,0.0003582521,0.0002357836,0.0001227925],"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":[0.002098874,0.000320368,0.01322924,0.005959467,0.002445476,0.001290045,0.0016026,0.009106901,0.05036825,0.01256988,0.6909103,0.2100985],"study_design_scores_gemma":[0.002155089,0.0004849523,0.029253,0.0009791473,0.001209873,0.002173028,0.0004633952,0.1198276,0.06345431,0.07552043,0.7035041,0.0009750359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.004817883,0.000633258,0.3908231,0.0003985132,0.0003970453,0.000318157,0.08597822,0.5136858,0.002947935],"genre_scores_gemma":[0.04246612,0.001025955,0.6877502,0.001291942,0.0001838523,0.003182756,0.1284624,0.1284273,0.007209549],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.07607163,"threshold_uncertainty_score":0.2544849,"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."}}