{"id":"W2197585008","doi":"10.1515/1544-6115.1760","title":"An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping","year":2012,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Clinical Research Institute","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research","keywords":"Multivariate statistics; Bayesian probability; Computer science; Bayesian hierarchical modeling; Artificial intelligence; Multivariate analysis; Data mining; Mathematics; Statistics; Machine learning; Bayesian inference","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001973945,0.0001616996,0.000164194,0.00006604338,0.0001083605,0.00001974088,0.00015321,0.0001912107,0.000007094799],"category_scores_gemma":[0.00006258419,0.0001507396,0.00003407261,0.00006837252,0.0001556972,0.000002308514,0.00008107624,0.00008785578,0.000001133625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007107106,"about_ca_system_score_gemma":0.00004045655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001698941,"about_ca_topic_score_gemma":0.000009833991,"domain_scores_codex":[0.9989101,0.0000650034,0.0002187144,0.00037634,0.00004757577,0.0003822356],"domain_scores_gemma":[0.9994233,0.00004487364,0.00004303078,0.0002332428,0.00005505552,0.0002005281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000916279,0.000475375,0.01840121,0.00005195056,0.00006673306,9.50764e-7,0.0002658684,0.001003009,0.7853522,0.1649829,0.000274506,0.02903361],"study_design_scores_gemma":[0.003700898,0.001785303,0.02932971,0.00004889499,0.0001932655,0.00004568146,0.001032678,0.6803252,0.03454491,0.08671774,0.1599851,0.002290645],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08863515,0.0004570514,0.9097567,0.00006435274,0.00003027834,0.0003732068,0.0005417133,0.000009460663,0.0001321041],"genre_scores_gemma":[0.7824324,0.0000972589,0.2161113,0.000219696,0.00005683828,0.0001141272,0.0009273713,0.0000125708,0.00002841286],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7508073,"threshold_uncertainty_score":0.6146984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01868155739456009,"score_gpt":0.3105318896602877,"score_spread":0.2918503322657277,"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."}}