{"id":"W4414553304","doi":"10.3390/biomedicines13102350","title":"Leveraging Machine Learning for Severity Level-Wise Biomarker Identification in Prostate Cancer Microarray Gene Expression Data","year":2025,"lang":"en","type":"article","venue":"Biomedicines","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Alberta; Prostate Cancer Canada; University of Calgary","funders":"","keywords":"Prostate cancer; Random forest; Biomarker; Support vector machine; Grading (engineering); Decision tree; Biomarker discovery; Microarray; Microarray analysis techniques","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":[0.0004088417,0.000142928,0.0001396247,0.0001843249,0.0001174356,0.00002708551,0.0003147022,0.00009274827,0.00001491511],"category_scores_gemma":[0.0001377296,0.0001238198,0.00003130111,0.0002870168,0.00006313866,0.00001215484,0.0001696355,0.00006979695,0.000001238075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003371213,"about_ca_system_score_gemma":0.0001260827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000120892,"about_ca_topic_score_gemma":0.00006630705,"domain_scores_codex":[0.9987449,0.00006599059,0.0003076127,0.0005812132,0.0001070109,0.0001932493],"domain_scores_gemma":[0.9991731,0.00001445779,0.0001310759,0.0005378718,0.00009946024,0.00004403276],"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":[0.0001639983,0.00004016509,0.02030838,0.00007819579,0.00001785209,4.428315e-7,0.00008624357,0.00001687912,0.9338138,0.000001351162,0.01352388,0.0319488],"study_design_scores_gemma":[0.001197867,0.00002237655,0.04808416,0.0001527922,0.00002012919,0.000001525857,0.0001749305,0.001934372,0.7063032,0.00003882615,0.2419127,0.0001571419],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9198039,0.01522145,0.05718154,0.004684857,0.001500175,0.001111986,0.000304455,0.00005059989,0.00014108],"genre_scores_gemma":[0.9888381,0.001329302,0.001062373,0.0002707281,0.0001529956,0.0002062114,0.00268236,0.00001790354,0.005439978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2283888,"threshold_uncertainty_score":0.5049225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05105901885078475,"score_gpt":0.3371224215794542,"score_spread":0.2860634027286694,"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."}}