{"id":"W2261944919","doi":"10.1093/bioinformatics/btv723","title":"PharmacoGx: an R package for analysis of large pharmacogenomic datasets","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":299,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Montreal Clinical Research Institute; Institute of Cancer Research; Ontario Institute for Cancer Research; University Health Network; University of Toronto; Princess Margaret Cancer Centre","funders":"Ontario Institute for Cancer Research; Fondation Brain Canada; Canadian Institutes of Health Research; Cancer Research Society","keywords":"Pharmacogenomics; Computer science; R package; Drug response; Source code; Cancer cell lines; Data science; Data mining; Open source; Realization (probability); Precision medicine; Software; Drug; Bioinformatics; Cancer; Medicine; Biology; Pharmacology; Programming language","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":[0.0007136444,0.0001091445,0.0002439196,0.000103843,0.00004435153,0.000008585373,0.0002086534,0.00009881089,0.00001770974],"category_scores_gemma":[0.000163272,0.0001014069,0.0001309593,0.0001760232,0.0000317765,0.00000838564,0.0000780322,0.00003259439,0.000008143728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001429957,"about_ca_system_score_gemma":0.00008459596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007663451,"about_ca_topic_score_gemma":0.00001913801,"domain_scores_codex":[0.9990977,0.00004561774,0.000409027,0.0001257831,0.00008365092,0.0002381932],"domain_scores_gemma":[0.9991306,0.00002146604,0.0002471565,0.0003291592,0.000129324,0.0001423144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007280416,0.00129662,0.124609,0.0002786641,0.01028892,0.000002006765,0.003278715,0.007189533,0.2668337,0.0008099534,0.5643791,0.02030573],"study_design_scores_gemma":[0.007038737,0.00136064,0.01749626,0.000007210535,0.00417472,0.00000814817,0.002686321,0.3780367,0.08469236,0.0001883931,0.5033684,0.0009420743],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8940732,0.0002057943,0.09776591,0.00006122491,0.0001783608,0.0002875479,0.006977583,0.00001162152,0.0004387122],"genre_scores_gemma":[0.9590099,0.00007711874,0.02461462,0.0006035496,0.0001048001,0.0000221899,0.01547804,0.00001175921,0.00007799462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3708472,"threshold_uncertainty_score":0.4135252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04707972317749436,"score_gpt":0.3602983026076302,"score_spread":0.3132185794301359,"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."}}