{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001853892,0.0004739088,0.0005664245,0.002501237,0.0004217704,0.0009955904,0.000422284,0.0006711256,0.001079285],"category_scores_gemma":[0.004079333,0.0002385902,0.0009085256,0.00232367,0.0005056139,0.0006812516,0.0004703142,0.001370784,0.0006074582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005436176,"about_ca_system_score_gemma":0.0005774557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001051352,"about_ca_topic_score_gemma":0.0013557,"domain_scores_codex":[0.9991404,0.000294966,0.00007626863,0.0002138391,0.0002067652,0.00006776412],"domain_scores_gemma":[0.9977509,0.001559999,0.0002760372,0.0001162411,0.0002434129,0.00005334528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003224494,0.0002977935,0.04045697,0.0006513676,0.0002290827,0.0004639148,0.0005945417,0.04775314,0.1438279,0.007102109,0.002992901,0.755308],"study_design_scores_gemma":[0.00007527107,0.0005112462,0.08489931,0.0002353744,0.0002533247,0.0008202401,0.0007911173,0.7661322,0.07373122,0.05553153,0.01687808,0.0001411532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1566823,0.004459325,0.8308892,0.001360777,0.0002145411,0.0002208375,0.001388172,0.001296011,0.00348898],"genre_scores_gemma":[0.3762677,0.002539816,0.6161453,0.0002723636,0.0002252498,0.0003133698,0.00205522,0.0001014038,0.002079531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002501237,"threshold_uncertainty_score":0.009804487,"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."}}