{"id":"W2107187602","doi":"10.1142/s0219720005001053","title":"CLUSTERING AND RE-CLUSTERING FOR PATTERN DISCOVERY IN GENE EXPRESSION DATA","year":2005,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Cluster analysis; Data mining; Single-linkage clustering; Computational biology; Computer science; Correlation clustering; Expression (computer science); Gene; Pairwise comparison; CURE data clustering algorithm; Pattern recognition (psychology); Biology; Artificial intelligence; Genetics","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":[0.000235836,0.00007185477,0.0001139916,0.00007808816,0.00003941718,0.000032876,0.0001228592,0.000067971,0.000001530984],"category_scores_gemma":[0.00002984029,0.00005759976,0.00002071742,0.00002614075,0.0000320825,0.00003529543,0.0001714131,0.00005107798,1.818297e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008175761,"about_ca_system_score_gemma":0.00003818204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001101176,"about_ca_topic_score_gemma":0.00001033344,"domain_scores_codex":[0.9993717,0.00001862114,0.0003658748,0.0001022374,0.00005234594,0.00008919711],"domain_scores_gemma":[0.9995468,0.00002857938,0.0002287778,0.0001027878,0.00005307997,0.00004003728],"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":[0.0006603385,0.0001418578,0.01266661,0.0002531416,0.0001006858,0.00000167465,0.0009113804,0.0396308,0.4106428,0.00009639492,0.003721135,0.5311732],"study_design_scores_gemma":[0.003862578,0.0007641216,0.012902,0.0001725977,0.00002441265,0.0002206469,0.0007290419,0.9376189,0.01761029,0.0006971735,0.02504389,0.0003543891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3248626,0.0006999281,0.6737098,0.0004872506,0.00009200687,0.00007870701,0.00003619148,0.000001286261,0.00003219246],"genre_scores_gemma":[0.9398024,0.0004064361,0.05909467,0.0003336391,0.0001795624,0.000002775269,0.0001608195,0.000004740981,0.00001496241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8979881,"threshold_uncertainty_score":0.234885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993371998511228,"score_gpt":0.3003840088938977,"score_spread":0.2704502889087854,"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."}}