{"id":"W2596091826","doi":"","title":"Predicting Cancer Survival Time Using an Artificial Neural Network with Gene Expression Data","year":2010,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial neural network; Artificial intelligence; Computer science; Cancer; Gene expression; Gene; Biology; Computational 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006399174,0.0004618466,0.0004486674,0.0007631265,0.0001627257,0.0004743826,0.0003808776,0.0005529038,0.0006171818],"category_scores_gemma":[0.00231829,0.0001688663,0.000401777,0.0007348025,0.0001963249,0.0003689036,0.000169671,0.0004866755,0.0001804052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003929902,"about_ca_system_score_gemma":0.0003365966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004105024,"about_ca_topic_score_gemma":0.003741304,"domain_scores_codex":[0.99984,0.00004084991,0.00001644123,0.00004286005,0.00003679802,0.0000230138],"domain_scores_gemma":[0.9990238,0.0007075196,0.00006788118,0.00003844262,0.0001353552,0.00002695894],"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":[0.001947002,0.0006403906,0.1299979,0.000168801,0.0002992062,0.0004409087,0.0000795403,0.6011885,0.01852358,0.0009263183,0.001854713,0.2439331],"study_design_scores_gemma":[0.00001083462,0.00005823212,0.006481726,0.000004537979,0.00003184455,0.00004524426,0.000009905782,0.99106,0.001573098,0.0005688814,0.0001493951,0.000006343934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8719056,0.0008401061,0.1236516,0.0004576878,0.0001187199,0.00005406468,0.001220359,0.0004320628,0.001319773],"genre_scores_gemma":[0.9788871,0.0002383851,0.01925218,0.00003792085,0.00003860101,0.00004529701,0.0008549627,0.000008302774,0.0006372218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004105024,"threshold_uncertainty_score":0.00816226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06669913901367736,"score_gpt":0.3220344351543474,"score_spread":0.2553352961406701,"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."}}