{"id":"W2953289747","doi":"10.12688/f1000research.8705.1","title":"Assessment of pharmacogenomic agreement","year":2016,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Clinical Research Institute; Hospital for Sick Children; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; Canadian Cancer Society Research Institute; National Institutes of Health; National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism; Higher Education Discipline Innovation Project; Government of Ontario; National Natural Science Foundation of China; Pershing Square Sohn Cancer Research Alliance; National Supercomputer Centre in Guangzhou; Cancer Research Society; National Heart, Lung, and Blood Institute; WorldQuant Foundation; Vallee Foundation; Ontario Institute for Cancer Research; Yale University; National Aeronautics and Space Administration","keywords":"Pharmacogenomics; Drug response; Drug; Medicine; Genomics; Computational biology; Pharmacology; Biology; Genetics; Gene; Genome","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.002823625,0.0002457721,0.000387756,0.0004956675,0.00006860679,0.0001606659,0.003613336,0.0001166079,0.0003264649],"category_scores_gemma":[0.00009416581,0.0002062895,0.0002181455,0.0002897116,0.000133243,0.0001884317,0.009031166,0.0006061195,0.0001067769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004200748,"about_ca_system_score_gemma":0.001976708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004186648,"about_ca_topic_score_gemma":0.00000118738,"domain_scores_codex":[0.9955904,0.000862645,0.0005414685,0.0008222284,0.00170905,0.0004741603],"domain_scores_gemma":[0.996839,0.0009123466,0.0002424041,0.001372686,0.0004534285,0.0001801452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004055059,0.0008554211,0.006774521,0.001267982,0.0007749682,0.0001289184,0.0005444592,0.04838351,0.04726991,0.552768,0.01703422,0.3241575],"study_design_scores_gemma":[0.00104832,0.0001608072,0.07992409,0.0004237796,0.00002478445,0.000009333951,0.00001113036,0.6091036,0.01887173,0.2811963,0.008528561,0.0006975369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03193551,0.0003461314,0.9353994,0.002085925,0.001091623,0.0008028183,0.0001059807,0.00008536447,0.02814732],"genre_scores_gemma":[0.8164248,0.0001666263,0.1815033,0.00008443397,0.0002886312,0.0001759184,0.00002397184,0.00003102069,0.00130126],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7844893,"threshold_uncertainty_score":0.9989836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09794534779577653,"score_gpt":0.4740802519048622,"score_spread":0.3761349041090857,"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."}}