{"id":"W2083152120","doi":"10.1109/cibcb.2014.6845530","title":"Using associators to generate ensemble biclustering from multiple evolved biclusterings","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Biclustering; Computer science; Cluster analysis; Block matrix; Data mining; Evolutionary algorithm; Hierarchical clustering; Algorithm; Data Matrix; Artificial intelligence; Pattern recognition (psychology); Canopy clustering algorithm; Correlation clustering","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":[0.0002419574,0.0001424098,0.0001619716,0.00008475483,0.0002149055,0.0003645511,0.0007665631,0.00005023841,0.00001531402],"category_scores_gemma":[0.00006941585,0.0001364578,0.00004271294,0.0003757318,0.0000107626,0.0003901449,0.000719137,0.0000686727,0.0001725415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006490056,"about_ca_system_score_gemma":0.00002375024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001335813,"about_ca_topic_score_gemma":0.0001003314,"domain_scores_codex":[0.9987587,0.00003540828,0.000215236,0.0004886807,0.0001887601,0.0003132103],"domain_scores_gemma":[0.9989174,0.0001115927,0.00006903883,0.000663994,0.00006039346,0.0001775754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000750103,0.0001601878,0.006829739,0.00001365896,0.00008532976,0.000005718995,0.002423258,0.009550324,0.5278314,0.007929753,0.005998844,0.4391643],"study_design_scores_gemma":[0.0001993489,0.00001942246,0.001563621,0.00001396658,0.00000436352,0.000001500344,0.00002117569,0.9723325,0.01550256,0.0002786087,0.00984979,0.0002131534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3014413,0.000004812834,0.6972519,0.0002769901,0.0001853856,0.00008270048,0.000009803164,0.0001950666,0.0005520945],"genre_scores_gemma":[0.4239654,6.307662e-7,0.575048,0.0006209603,0.0001172488,0.0000127739,0.00000665653,0.00001114135,0.0002172832],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9627821,"threshold_uncertainty_score":0.556459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0429895785078126,"score_gpt":0.269374615664514,"score_spread":0.2263850371567014,"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."}}