{"id":"W2032713750","doi":"10.1142/s0219720007002941","title":"EVALUATION OF NORMALIZATION AND PRE-CLUSTERING ISSUES IN A NOVEL CLUSTERING APPROACH: GLOBAL OPTIMUM SEARCH WITH ENHANCED POSITIONING","year":2007,"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":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University; Foundation for the National Institutes of Health; National Science Foundation","keywords":"Cluster analysis; Normalization (sociology); Data mining; Computer science; Correlation clustering; CURE data clustering algorithm; Outlier; Fuzzy clustering; Clustering high-dimensional data; Consensus clustering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00476009,0.001183434,0.001244148,0.0009511744,0.0006811916,0.00118836,0.001225077,0.00150034,0.001321566],"category_scores_gemma":[0.01269685,0.0004473001,0.0009108198,0.001447469,0.0008013708,0.001679494,0.001032476,0.001182548,0.0005977548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009977939,"about_ca_system_score_gemma":0.001144669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002283692,"about_ca_topic_score_gemma":0.003694613,"domain_scores_codex":[0.9983696,0.0006124107,0.00009657002,0.0002840117,0.0005556733,0.00008177226],"domain_scores_gemma":[0.9952362,0.002615518,0.0004194354,0.0005834663,0.001045983,0.00009947416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007556686,0.0002629045,0.002643427,0.0003521207,0.0002008344,0.0001167612,0.0002652013,0.7156141,0.04611878,0.0063946,0.001084783,0.2261907],"study_design_scores_gemma":[0.00004335418,0.0003063068,0.001095168,0.00001245494,0.0000389099,0.00007158729,0.00004137208,0.9712827,0.02486537,0.001375402,0.0008348751,0.00003250015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0646084,0.0002796162,0.9331698,0.0001878243,0.00004428067,0.00007904858,0.00004770466,0.0007734621,0.0008098133],"genre_scores_gemma":[0.1843899,0.0001685752,0.813991,0.0000680469,0.00002160513,0.0001244361,0.0001979223,0.0003167148,0.0007217549],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00476009,"threshold_uncertainty_score":0.02517402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02253895089369068,"score_gpt":0.3214746440522242,"score_spread":0.2989356931585335,"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."}}