{"id":"W3031121206","doi":"10.1371/journal.pcbi.1007882","title":"A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma","year":2020,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Sixth Framework Programme; Nordea-fonden; Danone","keywords":"Abundance (ecology); Quantitative trait locus; Computational biology; Bayesian probability; Human plasma; Biology; Trait; Locus (genetics); Genetics; Ecology; Computer science; Chemistry; Artificial intelligence; Chromatography; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001781667,0.0001338654,0.000312703,0.00006990167,0.00005067253,0.000003934376,0.0001436642,0.00016539,0.00002937486],"category_scores_gemma":[0.0003567571,0.0001360146,0.00007697133,0.0001524295,0.0001395608,0.000002599259,0.00007453826,0.0001120909,0.000008169704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001697733,"about_ca_system_score_gemma":0.00009357801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003016157,"about_ca_topic_score_gemma":0.0000355358,"domain_scores_codex":[0.9986388,0.0002317264,0.0004844824,0.0003577421,0.00006716679,0.0002201034],"domain_scores_gemma":[0.99943,0.00006014543,0.0002361527,0.00008897458,0.0001223314,0.00006241618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007482547,0.0001411025,0.02426643,0.00005261368,0.0001297309,0.000002708525,0.0005504924,0.01263335,0.9537521,0.007426591,0.0001877349,0.0007823153],"study_design_scores_gemma":[0.009720159,0.01159752,0.5038172,0.0003059718,0.00008268648,0.00003460435,0.002793854,0.2395087,0.1349613,0.09097905,0.003956915,0.002242009],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.967704,0.0001512761,0.02960431,0.001845711,0.00002266753,0.0002750898,0.0000664746,0.000009851459,0.0003206143],"genre_scores_gemma":[0.973756,0.000005452999,0.02536317,0.0004022558,0.00006602066,0.00004165838,0.0003389087,0.00001172192,0.00001482305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8187908,"threshold_uncertainty_score":0.5546515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04881396679701268,"score_gpt":0.2835588029157147,"score_spread":0.234744836118702,"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."}}