{"id":"W2140113562","doi":"10.1109/biotechno.2008.29","title":"Towards Better Outliers Detection for Gene Expression Datasets","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cluster analysis; Outlier; Computer science; Anomaly detection; Data mining; CURE data clustering algorithm; Pattern recognition (psychology); Task (project management); Medoid; Artificial intelligence; Correlation clustering; Engineering","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":[0.01472296,0.001646252,0.002523771,0.005158057,0.001009342,0.002821495,0.001916165,0.002599655,0.0005850504],"category_scores_gemma":[0.04420052,0.0006374695,0.001542772,0.003806781,0.001164206,0.002899189,0.001695045,0.001886207,0.0008407843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001127492,"about_ca_system_score_gemma":0.001008498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002053396,"about_ca_topic_score_gemma":0.001722342,"domain_scores_codex":[0.9900746,0.004019325,0.000819809,0.001733231,0.003013466,0.0003395833],"domain_scores_gemma":[0.9785131,0.0121598,0.002277582,0.002279779,0.004463946,0.0003057534],"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":[0.002832261,0.0005946539,0.03159031,0.001256364,0.0008540302,0.0003111539,0.001311614,0.2386076,0.1382495,0.006222192,0.004617916,0.5735524],"study_design_scores_gemma":[0.0001208481,0.0004308694,0.01934805,0.00007734566,0.0001343727,0.0004603626,0.0005766015,0.8555861,0.107231,0.009078515,0.006744292,0.0002116517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09440715,0.000705473,0.9001458,0.0003342988,0.00005551748,0.0001089923,0.0004407543,0.003396246,0.0004058336],"genre_scores_gemma":[0.191794,0.0002901272,0.8044621,0.0001006413,0.00003923125,0.0002017266,0.002284057,0.0004196937,0.0004084281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01472296,"threshold_uncertainty_score":0.0778634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011328874301446,"score_gpt":0.2549994400524776,"score_spread":0.2248861513094632,"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."}}