{"id":"W2074317305","doi":"10.1016/j.artmed.2008.05.002","title":"CARSVM: A class association rule-based classification framework and its application to gene expression data","year":2008,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Interpretability; Computer science; Support vector machine; Artificial intelligence; Discriminative model; Machine learning; Feature selection; Association rule learning; Classifier (UML); Associative property; Data mining; Pattern recognition (psychology); 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":[0.005033746,0.00130893,0.002048973,0.003240796,0.0006302419,0.002067976,0.002516705,0.001511856,0.003154934],"category_scores_gemma":[0.008326418,0.0005597536,0.001916807,0.002953075,0.000374972,0.001133741,0.0009141994,0.001989642,0.001920492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006425817,"about_ca_system_score_gemma":0.001983891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009592761,"about_ca_topic_score_gemma":0.008239491,"domain_scores_codex":[0.9977737,0.0005907377,0.0002517039,0.000479423,0.0008004497,0.0001039023],"domain_scores_gemma":[0.996272,0.00228872,0.0002148123,0.0002847098,0.0008294078,0.0001102583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004920224,0.0004043518,0.004984752,0.0004612088,0.0005964747,0.0002642462,0.000101023,0.08152176,0.008419619,0.004042678,0.02150203,0.8772098],"study_design_scores_gemma":[0.00004763629,0.0001221037,0.00142949,0.00005047538,0.0001357437,0.0001950943,0.00002620445,0.9818955,0.006070732,0.00354675,0.006440222,0.00004005372],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01455412,0.000989234,0.9613906,0.000316662,0.000190386,0.0002667167,0.002345684,0.01916441,0.0007821921],"genre_scores_gemma":[0.1007062,0.0004492169,0.8918351,0.0002586826,0.0001335925,0.0004385301,0.003912204,0.000595556,0.001670991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009592761,"threshold_uncertainty_score":0.02662134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08823450685544493,"score_gpt":0.3603690700165117,"score_spread":0.2721345631610668,"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."}}