{"id":"W7044130981","doi":"","title":"USING MACHINE LEARNING TECHNIQUES FOR FINDING MEANINGFUL TRANSCRIPTS IN PROSTATE CANCER PROGRESSION","year":2015,"lang":"en","type":"dissertation","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Prostate cancer; Feature selection; Support vector machine; Cancer; Feature (linguistics); Training set","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009677452,0.0004027275,0.0004983884,0.000403851,0.0003555,0.00003690062,0.0005084632,0.0006995432,0.00004112141],"category_scores_gemma":[0.0002450213,0.0004871486,0.0002314429,0.0002706396,0.00008030346,0.00005288039,0.0001317238,0.0008331558,0.000002639629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001987277,"about_ca_system_score_gemma":0.0003492768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002309413,"about_ca_topic_score_gemma":0.002634818,"domain_scores_codex":[0.9980504,0.0001806326,0.0003696186,0.0005286746,0.0004195435,0.0004511395],"domain_scores_gemma":[0.9984602,0.00002247336,0.0007063161,0.0003076423,0.00036771,0.0001356305],"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":[0.004913945,0.0001874806,0.2578328,0.00242691,0.0002984859,0.00002584427,0.01069688,0.001002139,0.7074597,0.00002700324,0.0002339169,0.01489484],"study_design_scores_gemma":[0.01465646,0.004233642,0.0703112,0.00976267,0.001444365,0.00008946055,0.01690742,0.01450563,0.7155649,0.0006699001,0.146112,0.005742399],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961883,0.001301571,0.0001373541,0.00005168816,0.0002167372,0.0009719454,0.0001444471,0.00005023786,0.0009376517],"genre_scores_gemma":[0.9590387,0.0004149903,0.02478512,0.00003378914,0.0001485126,0.00001376055,0.005537044,0.0001491628,0.009878855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1875216,"threshold_uncertainty_score":0.999758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03142827972941371,"score_gpt":0.3246979044188795,"score_spread":0.2932696246894658,"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."}}