{"id":"W2158012006","doi":"10.1109/tcbb.2005.17","title":"Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data","year":2005,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":204,"is_retracted":false,"has_abstract":true,"ca_institutions":"Pattern Discovery Technologies (Canada); University of Waterloo","funders":"Hong Kong Polytechnic University","keywords":"Cluster analysis; Tuple; Data mining; Selection (genetic algorithm); Computer science; Dimension (graph theory); Preprocessor; Expression (computer science); Data pre-processing; Feature selection; Artificial intelligence; 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":[0.0001798654,0.000114067,0.0001148449,0.00009416818,0.0001900702,0.00001578421,0.0001552586,0.0001534934,0.000005181738],"category_scores_gemma":[0.0000322269,0.0001035869,0.00002911981,0.00007769634,0.00008015646,0.00002624472,0.00001502013,0.00006697048,0.000001403587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001079652,"about_ca_system_score_gemma":0.00004742749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001305151,"about_ca_topic_score_gemma":0.0000131856,"domain_scores_codex":[0.9992346,0.00003020971,0.0003189165,0.0002362152,0.00006719708,0.0001129001],"domain_scores_gemma":[0.9993457,0.00006960911,0.0001570425,0.0002511842,0.000125485,0.00005096442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003917217,0.0001891805,0.001120024,0.0001603329,0.0001136922,3.449707e-8,0.0002712981,0.02776298,0.8021449,0.0002127008,0.001587393,0.1660457],"study_design_scores_gemma":[0.002515468,0.0008388699,0.00738157,0.00007560835,0.00008383216,0.00005471209,0.000291622,0.65176,0.3149825,0.001006928,0.02053387,0.0004750717],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06601627,0.0001249288,0.9328046,0.0004388819,0.0001117967,0.0002158091,0.0002478316,0.00001419637,0.00002568646],"genre_scores_gemma":[0.8119479,0.0002040643,0.1868857,0.00015879,0.00008654338,0.00002840132,0.0006401439,0.000007301419,0.00004118341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7459316,"threshold_uncertainty_score":0.422415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04644094477285871,"score_gpt":0.3179398821988953,"score_spread":0.2714989374260366,"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."}}