{"id":"W2794682008","doi":"","title":"Using Gene Expression to Predict Tumor Location in Prostate Cancer Tissue","year":2018,"lang":"en","type":"article","venue":"Research in Computational Molecular Biology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Prostate cancer; Cancer; Gene expression; Computer science; Cancer research; Gene; Oncology; Internal medicine; Medicine; Biology; Genetics","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":[0.000980728,0.00008061844,0.0001633317,0.0005466428,0.00005998186,0.00001393049,0.0001037836,0.00004874621,0.0000311819],"category_scores_gemma":[0.0005120933,0.0000731556,0.00001416987,0.0007426511,0.0002043056,0.00002646523,0.0001096758,0.0003810572,0.00001838365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002247885,"about_ca_system_score_gemma":0.0004052883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005244648,"about_ca_topic_score_gemma":0.00001565225,"domain_scores_codex":[0.9984297,0.0003295176,0.0002372319,0.0003339154,0.0003076369,0.0003620081],"domain_scores_gemma":[0.9992443,0.0001265904,0.00003119521,0.0001194465,0.0003346382,0.0001438592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003430668,0.0001430199,0.06198596,0.00008249559,0.00001424096,0.0003129978,0.0002903574,0.08115009,0.8338355,0.001101944,0.0001738324,0.02056653],"study_design_scores_gemma":[0.003341564,0.001793313,0.05910794,0.001393147,0.000008993137,0.0002064098,0.0000784068,0.8264835,0.0782446,0.02637405,0.002665628,0.0003024583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8836161,0.0003044711,0.1110194,0.004315178,0.00009497087,0.0005095671,0.000003790711,0.00001300106,0.0001235276],"genre_scores_gemma":[0.9492231,0.000008957631,0.04990283,0.0005569343,0.0001712233,0.00005892062,0.0000447134,0.00001586373,0.00001748145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7555909,"threshold_uncertainty_score":0.2983199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05918206320353468,"score_gpt":0.482250000141232,"score_spread":0.4230679369376973,"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."}}