{"id":"W2950286149","doi":"10.1158/0008-5472.can-17-0096","title":"Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy","year":2017,"lang":"en","type":"article","venue":"Cancer Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research; Hospital for Sick Children; McGill University; Princess Margaret Cancer Centre; University of Toronto; Université de Montréal; Lunenfeld-Tanenbaum Research Institute; Montreal Clinical Research Institute","funders":"","keywords":"Pharmacogenomics; Cancer; Drug; Taxonomy (biology); Medicine; Drug response; Computational biology; Biology; Internal medicine; Pharmacology; Ecology","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":[0.00203911,0.0001800449,0.0002296469,0.0002629086,0.0009776604,0.0008980591,0.003153987,0.00004340304,0.0002615594],"category_scores_gemma":[0.0002452912,0.0001610491,0.00008245192,0.0004218812,0.0001783032,0.0009330906,0.002236014,0.0005731935,0.000215015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006651292,"about_ca_system_score_gemma":0.001263565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001933775,"about_ca_topic_score_gemma":0.001708547,"domain_scores_codex":[0.9972154,0.0003895757,0.0002190912,0.0007067153,0.0007330344,0.0007361525],"domain_scores_gemma":[0.9973861,0.0003753731,0.0000918106,0.001078015,0.0007298971,0.0003388184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002651218,0.0003808821,0.01634777,0.0001171088,0.0002805128,0.00008008157,0.01971198,0.03849317,0.01833236,0.05635315,0.2620255,0.5876123],"study_design_scores_gemma":[0.001160919,0.00006269251,0.01411252,0.0001851999,0.000009869803,0.000003081828,0.0004068737,0.08702909,0.06499697,0.008144652,0.823267,0.0006211287],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4983955,0.0009292135,0.4622617,0.02057646,0.00208832,0.00157481,0.0001818385,0.0001124389,0.01387973],"genre_scores_gemma":[0.9169312,0.0004642175,0.06355458,0.001668675,0.001286881,0.003692285,0.000005747082,0.0000548603,0.01234159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5869913,"threshold_uncertainty_score":0.8660004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1766503821741809,"score_gpt":0.5009506009118767,"score_spread":0.3243002187376958,"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."}}