{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008960743,0.00008812708,0.00008116728,0.00006810345,0.0002239716,0.00004057151,0.000283479,0.00006148696,0.00004098217],"category_scores_gemma":[0.00002169227,0.00006885092,0.00005063682,0.00009644705,0.00001796277,0.0005853988,0.0001103833,0.00005246495,0.00009837059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001548007,"about_ca_system_score_gemma":0.00001922255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000165131,"about_ca_topic_score_gemma":0.000002529022,"domain_scores_codex":[0.9992172,0.00002154377,0.000128893,0.0002871911,0.0001643862,0.0001808392],"domain_scores_gemma":[0.9994922,0.00002687707,0.0000393119,0.0003269865,0.00004095095,0.00007365623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003244993,0.00008197501,0.0001098247,0.00001484936,0.000009293432,0.00001187897,0.0003808724,0.00002743818,0.6114756,0.00006966163,0.1509697,0.2368164],"study_design_scores_gemma":[0.000380825,0.00005984549,0.0003669007,0.000008819336,0.000002169407,0.00002343521,0.00001243456,0.007717361,0.9646652,0.0007015091,0.0259306,0.0001308664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05965414,0.00001447522,0.938603,0.0004524346,0.0003702799,0.0001720016,0.0000291317,0.0001659895,0.0005385526],"genre_scores_gemma":[0.627208,0.00002820303,0.3699015,0.002038963,0.0001594448,0.0001101094,0.0001533982,0.00001161824,0.0003887397],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5687015,"threshold_uncertainty_score":0.2807659,"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."}}