{"id":"W3106605508","doi":"10.3389/fgene.2020.550894","title":"Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer","year":2020,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Prostate Cancer Canada; Université de Montréal; Université Laval; Centre hospitalier de l'Université Laval","funders":"Terry Fox Research Institute; Université Laval","keywords":"Prostate cancer; Computer science; Random forest; Artificial intelligence; Biochemical recurrence; Machine learning; Gene signature; Computational biology; Data mining; Cancer; Medicine; Biology; Gene; Internal medicine; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009463857,0.00008702292,0.0001761761,0.00004020734,0.00001534169,0.000002221853,0.0001264717,0.0001356138,0.000001442645],"category_scores_gemma":[0.00003193042,0.00009418504,0.00004819074,0.0001507257,0.00009224055,0.000001377495,0.00002754771,0.0000874125,2.452198e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008540252,"about_ca_system_score_gemma":0.00004444934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001861721,"about_ca_topic_score_gemma":0.00000585421,"domain_scores_codex":[0.9992422,0.0000570791,0.0003497224,0.0001925561,0.00006672717,0.0000916908],"domain_scores_gemma":[0.9994825,0.000002981239,0.0002677006,0.0001216147,0.00009904521,0.00002614244],"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":[0.00004605946,0.00005929681,0.1182389,0.0000941091,0.00003114853,1.415025e-7,0.00013026,0.001629959,0.878297,0.00001203749,0.0002040874,0.00125701],"study_design_scores_gemma":[0.0003591253,0.0002181972,0.002210594,0.00002658016,0.00004771603,5.22454e-7,0.00004409098,0.02665737,0.9696157,0.00009734201,0.000632285,0.00009047554],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8659234,0.006237075,0.127172,0.00008118835,0.00003499215,0.0004279829,0.000111161,0.000004614539,0.000007566489],"genre_scores_gemma":[0.9890139,0.001245755,0.009418198,0.00002983526,0.000008120672,0.00003279381,0.0002143503,0.0000144967,0.00002254975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1230905,"threshold_uncertainty_score":0.3840755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007839765308474802,"score_gpt":0.2385013453308015,"score_spread":0.2306615800223267,"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."}}