{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009313316,0.003403332,0.002838389,0.004190625,0.000745329,0.002480224,0.004366785,0.001318512,0.0701431],"category_scores_gemma":[0.04207283,0.002158405,0.003750573,0.003828852,0.001539836,0.002610058,0.004656725,0.004541751,0.03217839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009964121,"about_ca_system_score_gemma":0.005176151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003594293,"about_ca_topic_score_gemma":0.004488953,"domain_scores_codex":[0.9954066,0.002155835,0.0004233401,0.0008426249,0.0008956262,0.0002759507],"domain_scores_gemma":[0.9744229,0.01933285,0.001926651,0.002358252,0.001222221,0.0007370897],"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":[0.001269814,0.0001102801,0.01048159,0.004288276,0.003167714,0.0006740405,0.0004912397,0.01784685,0.003279683,0.0147117,0.8437666,0.09991225],"study_design_scores_gemma":[0.002381721,0.000392903,0.01668476,0.001007207,0.001599506,0.001581758,0.0001900791,0.162783,0.009509526,0.1063263,0.6969984,0.0005447789],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.004132795,0.001083183,0.488666,0.002097548,0.0005407597,0.0008644959,0.1475522,0.3499709,0.005092045],"genre_scores_gemma":[0.05816478,0.00144027,0.6575598,0.002083457,0.0004427856,0.007700899,0.127881,0.1380951,0.006632041],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0701431,"threshold_uncertainty_score":0.234652,"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."}}