{"id":"W2997760522","doi":"10.1007/s10637-020-00892-8","title":"Patient selection for a developmental therapeutics program using whole genome and Transcriptome analysis","year":2020,"lang":"en","type":"article","venue":"Investigational New Drugs","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency","funders":"","keywords":"Clinical trial; Transcriptome; Medicine; Genome; Precision medicine; Oncology; Bioinformatics; Selection (genetic algorithm); Internal medicine; Gene; Computational biology; Gene expression; Biology; Pathology; Genetics; Computer science","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":[0.00003183126,0.00009837344,0.00009624588,0.00003498467,0.00008207098,0.00004128692,0.00004448764,0.00005711334,0.000005107122],"category_scores_gemma":[0.00002104253,0.0001066243,0.00006558265,0.00019079,0.00004786434,0.000004275705,0.00002213618,0.0000340122,9.213015e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002630213,"about_ca_system_score_gemma":0.0002891968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002892667,"about_ca_topic_score_gemma":0.00002374542,"domain_scores_codex":[0.9993911,0.00001034027,0.0001538004,0.0002352428,0.00008887998,0.0001206142],"domain_scores_gemma":[0.9996885,0.0000114429,0.00005154118,0.00003871097,0.00006440696,0.0001453826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006308292,0.00003471566,0.004510707,0.00001691936,0.0004348989,1.999413e-7,0.001140169,0.003929249,0.9826608,0.0001553647,0.0003792555,0.00667463],"study_design_scores_gemma":[0.002535738,0.001321687,0.01171505,0.00001438958,0.001091499,0.00001861575,0.0006274463,0.07083862,0.1705693,0.001840763,0.7384973,0.000929638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865799,0.0003893542,0.01106247,0.001444458,0.00002913275,0.0003325439,0.0001162508,0.00001291019,0.00003301562],"genre_scores_gemma":[0.9374669,0.00003330902,0.0581733,0.003445821,0.0002218655,0.00003526228,0.0005780542,0.00001572079,0.00002971626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8120915,"threshold_uncertainty_score":0.4348012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02847075282832528,"score_gpt":0.2639221654190349,"score_spread":0.2354514125907096,"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."}}