{"id":"W2099722147","doi":"10.1109/iccabs.2014.6863912","title":"Breast cancer subtype identification using machine learning techniques","year":2014,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Gene selection; Breast cancer; Machine learning; Classifier (UML); Selection (genetic algorithm); Disease; Identification (biology); Artificial intelligence; Feature selection; Computer science; Cancer; Gene; Bioinformatics; Medicine; Biology; Internal medicine; Gene expression; Microarray analysis techniques","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":[0.001036073,0.0003383855,0.0006216123,0.002055039,0.0003768352,0.0007737004,0.000448215,0.0003089852,0.0008654564],"category_scores_gemma":[0.002668937,0.0001209535,0.0007064066,0.001419087,0.0001376134,0.0003994034,0.0003377865,0.0005522662,0.0008343438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004039994,"about_ca_system_score_gemma":0.0007153078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004215734,"about_ca_topic_score_gemma":0.006038907,"domain_scores_codex":[0.9994203,0.000193228,0.00005030319,0.000105798,0.0001582502,0.00007219576],"domain_scores_gemma":[0.9991026,0.0003983903,0.0001162927,0.0001145669,0.0002315608,0.00003665848],"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":[0.0004071773,0.000320826,0.171288,0.0001965638,0.0002018638,0.0002869767,0.0001548358,0.03373703,0.07712316,0.002310198,0.00381664,0.7101567],"study_design_scores_gemma":[0.00004980186,0.0003334784,0.1231777,0.0000790884,0.0002265771,0.0007664147,0.0004243327,0.8132355,0.03944194,0.01108744,0.01111199,0.00006572297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4395904,0.002974661,0.5458611,0.0008695877,0.0001352694,0.0005509682,0.002091321,0.001720986,0.006205629],"genre_scores_gemma":[0.6912178,0.0006977516,0.3040442,0.0001984025,0.00007122431,0.0001840384,0.002272711,0.000042101,0.00127187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004215734,"threshold_uncertainty_score":0.00838244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323772967003517,"score_gpt":0.2842986828636899,"score_spread":0.2710609531936548,"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."}}