{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01378336,0.0007408624,0.000797567,0.002095758,0.0004073867,0.002282748,0.0009589841,0.0009557173,0.002318552],"category_scores_gemma":[0.02737716,0.0003363584,0.0006227428,0.001367287,0.001666087,0.00144634,0.001672288,0.002003364,0.0006431542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385625,"about_ca_system_score_gemma":0.004424566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002242988,"about_ca_topic_score_gemma":0.002401474,"domain_scores_codex":[0.994347,0.003697215,0.0003016415,0.0004914901,0.001045413,0.0001171935],"domain_scores_gemma":[0.9784688,0.01521609,0.002928678,0.001827259,0.001114688,0.0004445729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008327765,0.0005486828,0.1402297,0.001671524,0.0006657604,0.0005174177,0.0004127764,0.04614928,0.02693087,0.02694273,0.01054157,0.744557],"study_design_scores_gemma":[0.0009677414,0.003742829,0.09593277,0.003546656,0.001669494,0.003409331,0.001142633,0.2369859,0.1150451,0.3130609,0.224065,0.0004314865],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2842446,0.02951052,0.5681815,0.08361896,0.0007485396,0.001307021,0.0052687,0.003681724,0.02343844],"genre_scores_gemma":[0.6622861,0.01176054,0.3125976,0.009608802,0.0004353818,0.0003573479,0.001499303,0.0002255904,0.001229249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01378336,"threshold_uncertainty_score":0.07289422,"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."}}