{"id":"W2901823560","doi":"10.1200/cci.18.00077","title":"Leveraging Human Genetics to Guide Cancer Drug Development","year":2018,"lang":"en","type":"article","venue":"JCO Clinical Cancer Informatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Cancer Research","funders":"Cancer Research UK","keywords":"Druggability; Drug development; Cancer; Drug repositioning; Genome-wide association study; Computational biology; Biology; Human genome; Drug discovery; Genome; Gene; Genetics; Bioinformatics; Drug; Single-nucleotide polymorphism; Genotype; Pharmacology","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":[0.001061965,0.0001979561,0.0003143746,0.00004807647,0.0002568732,0.00003117643,0.0003753827,0.0002140233,0.0001493946],"category_scores_gemma":[0.0002856319,0.0001835357,0.0001080144,0.0001272752,0.0001301518,0.000004994073,0.0003363789,0.000159137,0.0001190654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009445789,"about_ca_system_score_gemma":0.0004666462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001331656,"about_ca_topic_score_gemma":0.0009630119,"domain_scores_codex":[0.9976751,0.00007534917,0.001361246,0.000246717,0.0001893035,0.0004523025],"domain_scores_gemma":[0.9985982,0.00004690866,0.000340888,0.0004215433,0.0003550039,0.0002374299],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004046248,0.00008704227,0.6000391,0.00004686641,0.0002322163,5.329071e-7,0.00257622,0.001260428,0.00146264,0.00003720618,0.3041599,0.09005739],"study_design_scores_gemma":[0.000496991,0.0001664686,0.1412483,0.00003146663,0.00003016987,8.957278e-7,0.0003012459,0.0004277437,0.004308489,0.00004624422,0.8526238,0.0003180937],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863976,0.0003168336,0.007127922,0.0006935633,0.0009124842,0.0002545578,0.00002057184,0.00002232965,0.004254146],"genre_scores_gemma":[0.9235422,0.000840222,0.05026978,0.01619991,0.002444807,0.0001767168,0.00009077045,0.0000398539,0.006395721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5484639,"threshold_uncertainty_score":0.7484369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07595552485948649,"score_gpt":0.4327263945480103,"score_spread":0.3567708696885238,"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."}}