{"id":"W1991772956","doi":"10.1142/s0219720010004495","title":"A MULTI-STRATEGY APPROACH TO INFORMATIVE GENE IDENTIFICATION FROM GENE EXPRESSION DATA","year":2010,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Biotechnology Research Institute; National Research Council Canada","funders":"","keywords":"Identification (biology); Computer science; Selection (genetic algorithm); Gene prediction; Data mining; Gene; Computational biology; Genomics; Artificial intelligence; Biology; Genome; 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":[0.005609517,0.001589768,0.001594662,0.007142735,0.0007809071,0.00154505,0.001540476,0.0009722729,0.001043032],"category_scores_gemma":[0.007701147,0.0005743522,0.002697886,0.003494663,0.0008516837,0.0009763095,0.001524492,0.00148258,0.000648224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000702492,"about_ca_system_score_gemma":0.001497522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00144168,"about_ca_topic_score_gemma":0.00263832,"domain_scores_codex":[0.9963025,0.001390207,0.0002454738,0.0007477364,0.001146499,0.0001675697],"domain_scores_gemma":[0.9957054,0.00272957,0.0003246962,0.0005183018,0.0005922377,0.0001298763],"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":[0.0006345874,0.0005960695,0.01843111,0.001008067,0.001654893,0.0009619601,0.0007784435,0.05130881,0.2142043,0.01810809,0.003563381,0.6887502],"study_design_scores_gemma":[0.0001571296,0.0008533256,0.02032395,0.0001077144,0.0005485321,0.002140989,0.0002466124,0.8159371,0.1005047,0.04660551,0.0123029,0.0002715671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008765259,0.0002686006,0.9896327,0.00006699018,0.00001301221,0.000146885,0.0001943729,0.0006553269,0.0002567017],"genre_scores_gemma":[0.08879731,0.0001600868,0.9090142,0.0001525377,0.0000266962,0.0004120433,0.0007909514,0.0001415304,0.0005047341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007142735,"threshold_uncertainty_score":0.0296663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03657747354787876,"score_gpt":0.3069283622176294,"score_spread":0.2703508886697506,"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."}}