{"id":"W2950449244","doi":"10.1158/0008-5472.can-19-0349","title":"Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts","year":2019,"lang":"en","type":"article","venue":"Cancer Research","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Vector Institute; Université du Québec à Montréal; Hospital for Sick Children; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Pharmacogenomics; Precision medicine; Computational biology; Concordance; Drug response; Medicine; Biomarker; Biomarker discovery; In vivo; Bioinformatics; Personalized medicine; Drug discovery; Oncology; Drug; Biology; Gene; Pharmacology; Genetics; Pathology; Proteomics","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":[0.0002161925,0.0001031809,0.0002147923,0.0002392702,0.00004497867,0.00001916576,0.0002421956,0.00007674935,0.0003318284],"category_scores_gemma":[0.00006829904,0.00009439776,0.0001504375,0.0006429843,0.0001246982,0.000002548194,0.0002047161,0.0001428695,0.00001737666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006924512,"about_ca_system_score_gemma":0.0002725956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003682852,"about_ca_topic_score_gemma":0.0003024889,"domain_scores_codex":[0.9989019,0.00007679289,0.0001911118,0.0003218353,0.0002210566,0.0002873247],"domain_scores_gemma":[0.9990069,0.00005628506,0.00006910107,0.0003623477,0.0004136963,0.00009167317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001962564,0.00004630775,0.008131078,0.00001448669,0.0007434097,0.000001022658,0.0003023828,0.001397306,0.9834305,0.00009977778,0.00215023,0.003487221],"study_design_scores_gemma":[0.0004078027,0.0003132588,0.001439647,0.00001204238,0.0001136628,3.225525e-7,0.0004734095,0.0001967928,0.9547514,0.00003059153,0.04212821,0.0001328696],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995633,0.002200771,0.00006651341,0.00007166326,0.0002054812,0.0002746831,0.0002168532,0.000003051376,0.001327993],"genre_scores_gemma":[0.9964142,0.002746081,0.00008887943,0.0001163563,0.0001151537,0.0000573432,0.0001281117,0.00001783461,0.0003159982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03997798,"threshold_uncertainty_score":0.3849429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02784941580998801,"score_gpt":0.3754003004453931,"score_spread":0.3475508846354051,"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."}}