{"id":"W2202153558","doi":"10.1109/bibm.2015.7359680","title":"A two-step logistic regression algorithm for identifying individual-cancer-related genes","year":2015,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Logistic regression; Identification (biology); Computer science; Locus (genetics); Regression; Algorithm; Gene; Artificial intelligence; Set (abstract data type); Machine learning; Mathematics; Biology; Genetics; Statistics","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":[0.004335465,0.001240334,0.001276664,0.001735046,0.0005616626,0.0009727023,0.002140527,0.001276797,0.002646726],"category_scores_gemma":[0.009246378,0.0004874743,0.001157797,0.00147076,0.0003466623,0.0009883238,0.001050908,0.001992492,0.002257936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004670577,"about_ca_system_score_gemma":0.001451303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002599244,"about_ca_topic_score_gemma":0.002641331,"domain_scores_codex":[0.9983358,0.0005969133,0.0001552612,0.0004579758,0.0003263373,0.0001277023],"domain_scores_gemma":[0.9969856,0.001968586,0.0002322267,0.0001763659,0.0005594264,0.0000777262],"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":[0.0006278951,0.0004703044,0.01832072,0.0002254014,0.0002671658,0.0003825197,0.0001822842,0.1795663,0.01329514,0.002595996,0.007518347,0.7765478],"study_design_scores_gemma":[0.00006183524,0.0001202223,0.00189307,0.00001401282,0.00003536098,0.0002222464,0.00003744521,0.9903926,0.003804592,0.001747988,0.001645037,0.00002564326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02623129,0.0002830211,0.9698061,0.000301845,0.00003738529,0.0001954928,0.0002178994,0.002259697,0.0006671958],"genre_scores_gemma":[0.1955414,0.0002267221,0.7986035,0.0002420215,0.00005910947,0.0007128961,0.001246615,0.0002051129,0.003162538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004335465,"threshold_uncertainty_score":0.02292842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1121183806425608,"score_gpt":0.3765901859783027,"score_spread":0.264471805335742,"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."}}