{"id":"W1964856813","doi":"10.1145/1982185.1982209","title":"Classifying microarray data with association rules","year":2011,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Association rule learning; Computer science; Associative property; Classifier (UML); Data mining; Artificial intelligence; Microarray analysis techniques; Support vector machine; Machine learning; Pattern recognition (psychology); Feature (linguistics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001236954,0.00006730487,0.00004956716,0.00001916815,0.00004668052,0.00001487905,0.0002355958,0.00008037167,0.0001271332],"category_scores_gemma":[0.00002503556,0.00005075576,0.00001368656,0.00004133309,0.00001472069,0.000005047826,0.00008332331,0.00003829359,0.00003163268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001329558,"about_ca_system_score_gemma":0.00004659334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001811079,"about_ca_topic_score_gemma":0.00004406945,"domain_scores_codex":[0.9994164,0.00002537556,0.0000851794,0.0002710679,0.00008793846,0.0001141031],"domain_scores_gemma":[0.999313,0.000002568239,0.00007433261,0.0005203352,0.00005140021,0.00003835257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006296377,0.0000491855,0.02938935,0.000005080919,0.00004277573,4.058348e-7,0.00006269504,4.778764e-7,0.9186077,0.0001884258,0.04714808,0.004442879],"study_design_scores_gemma":[0.0004804043,0.000107417,0.03693091,0.00000999448,0.00002177907,0.000003978052,0.0002791771,0.00004369297,0.6346799,0.00005333112,0.3271864,0.00020306],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7652549,0.0005655293,0.04600166,0.0006433186,0.0003468218,0.0002900466,0.00005476764,0.00008012152,0.1867628],"genre_scores_gemma":[0.9819172,0.00008624524,0.00908247,0.000329662,0.0001109113,0.00001102504,0.000344148,0.00001303643,0.008105279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2839278,"threshold_uncertainty_score":0.206976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05606819587348594,"score_gpt":0.2659678905316183,"score_spread":0.2098996946581324,"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."}}