{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007930526,0.003006086,0.003762712,0.007904905,0.001985296,0.002643944,0.004909215,0.002765824,0.002149489],"category_scores_gemma":[0.02264409,0.001528396,0.004418403,0.009844454,0.001863089,0.00283082,0.00277586,0.004423226,0.003446892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001823181,"about_ca_system_score_gemma":0.002629834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005229796,"about_ca_topic_score_gemma":0.004830847,"domain_scores_codex":[0.9878433,0.004175007,0.001181287,0.003297694,0.003166606,0.0003362292],"domain_scores_gemma":[0.9896849,0.004646804,0.001016158,0.002405105,0.002064922,0.0001821312],"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":[0.0004667711,0.000305623,0.002449723,0.001728282,0.00106475,0.0004769666,0.001082027,0.1887522,0.02594523,0.03289919,0.01094001,0.7338892],"study_design_scores_gemma":[0.00005221336,0.0001038705,0.001551664,0.00009581212,0.0001036901,0.0004331416,0.0001600484,0.9340844,0.0119465,0.03751905,0.01379691,0.0001527513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001222646,0.0003419327,0.9958235,0.00008737418,0.0000483044,0.0002276664,0.0001436394,0.001907096,0.0001979064],"genre_scores_gemma":[0.01173424,0.0002524912,0.9861145,0.00006614198,0.00003324242,0.0005425861,0.0006631865,0.0002604401,0.0003330922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007930526,"threshold_uncertainty_score":0.04194117,"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."}}