{"id":"W2749510389","doi":"10.1145/3107411.3108226","title":"Predicting Breast Cancer Outcome under Different Treatments by Feature Selection Approaches","year":2017,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Feature selection; Mechanism (biology); Breast cancer; Disease; Computer science; Cancer; Selection (genetic algorithm); Feature (linguistics); Machine learning; Artificial intelligence; Outcome (game theory); Bioinformatics; Computational biology; Medicine; Internal medicine; Biology; Mathematics","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":[0.002392183,0.0008870105,0.0009827047,0.001660398,0.0003114904,0.0006328524,0.0004317354,0.0003880023,0.0007312036],"category_scores_gemma":[0.003572011,0.0001146336,0.001222594,0.0008928912,0.0002062694,0.0003310996,0.0003645852,0.0006940845,0.0002008917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004696959,"about_ca_system_score_gemma":0.0005719992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003166141,"about_ca_topic_score_gemma":0.002606256,"domain_scores_codex":[0.999121,0.0004327691,0.00007083882,0.0001336702,0.0001186175,0.0001230107],"domain_scores_gemma":[0.9981734,0.001280636,0.0001784591,0.0000907553,0.000189452,0.00008729017],"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":[0.003774571,0.001274198,0.4005645,0.0001713073,0.001422392,0.0005354931,0.0001408714,0.1690727,0.01378038,0.0004739662,0.006301798,0.4024878],"study_design_scores_gemma":[0.0001773998,0.001135481,0.1295081,0.00002378155,0.0004842964,0.0002865047,0.0001144167,0.8593945,0.004982795,0.002938918,0.0008916066,0.00006212422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9041345,0.0009902458,0.08970334,0.001225166,0.0001176864,0.0001664116,0.00206924,0.0006726685,0.0009207068],"genre_scores_gemma":[0.9916486,0.00008315748,0.006686615,0.0000660322,0.00004408604,0.000058651,0.001206332,0.00001335299,0.0001932848],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003166141,"threshold_uncertainty_score":0.01265126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04266799858012966,"score_gpt":0.3010430215529161,"score_spread":0.2583750229727864,"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."}}