{"id":"W4410537121","doi":"10.1109/ictcs65341.2025.10989373","title":"Machine Learning-Driven Prediction of Gleason Score 7 Prostate Cancer Patterns Using Multi-Omics Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Prostate cancer; Omics; Computer science; Artificial intelligence; Prostate; Cancer; Machine learning; Medicine; Bioinformatics; Internal medicine; Biology","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":[0.0007942156,0.000533264,0.0004454236,0.0008334721,0.0001847152,0.0004792452,0.0004835962,0.0003672789,0.0005610556],"category_scores_gemma":[0.001540032,0.0002046374,0.0006478266,0.0005848436,0.000193316,0.0004417498,0.0004087899,0.0008229943,0.0003085795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004859751,"about_ca_system_score_gemma":0.0006113569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005499895,"about_ca_topic_score_gemma":0.007296662,"domain_scores_codex":[0.9997478,0.00006340762,0.00001198913,0.00008804368,0.0000441658,0.00004443251],"domain_scores_gemma":[0.9994932,0.0002746493,0.00007063837,0.00004320805,0.00008722482,0.00003100724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005226664,0.0003638313,0.08409104,0.0001384535,0.0002084622,0.0003125083,0.00009449452,0.6822726,0.02365045,0.001859955,0.003492136,0.2029933],"study_design_scores_gemma":[0.000004320279,0.00002854616,0.005711006,0.000005572247,0.000008734065,0.00002986333,0.000009829736,0.9910769,0.001985712,0.0008341267,0.0002996091,0.000005652219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6426575,0.001135816,0.3488834,0.000889322,0.0001046739,0.00009949391,0.003041355,0.001636368,0.001552138],"genre_scores_gemma":[0.9494705,0.0002064382,0.04581698,0.0001334334,0.00004966798,0.0000532571,0.003240386,0.00002949485,0.0009997443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005499895,"threshold_uncertainty_score":0.01093578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0292858341038989,"score_gpt":0.2808079343737918,"score_spread":0.2515221002698929,"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."}}