{"id":"W2170192148","doi":"10.1093/bioinformatics/18.suppl_1.s111","title":"Binary tree-structured vector quantization approach toclustering and visualizing microarray data","year":2002,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research","funders":"National Cancer Institute","keywords":"Cluster analysis; Computer science; Vector quantization; Data mining; Binary tree; Binary data; Tree (set theory); k-d tree; Artificial intelligence; Pattern recognition (psychology); Binary number; Mathematics; Algorithm; Tree traversal","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":[0.002027137,0.0008601705,0.0008151963,0.001876538,0.0005563579,0.001490257,0.001445542,0.0007626714,0.004828113],"category_scores_gemma":[0.005551936,0.0003261353,0.0006236641,0.002938428,0.0008622383,0.001509634,0.001189053,0.001010581,0.001450635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008447776,"about_ca_system_score_gemma":0.001159352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002616839,"about_ca_topic_score_gemma":0.002045198,"domain_scores_codex":[0.9985886,0.0005440829,0.0001263736,0.0001996822,0.0004714329,0.00006974097],"domain_scores_gemma":[0.9973581,0.001017086,0.0002471603,0.0002794925,0.001005725,0.00009239688],"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":[0.0004124758,0.0001868185,0.002727367,0.001742717,0.0001490557,0.0002292538,0.001017669,0.2921551,0.05657519,0.07490484,0.01726449,0.552635],"study_design_scores_gemma":[0.00002877467,0.00005963882,0.0009373881,0.00006753531,0.00001480459,0.00008712929,0.0001284571,0.9427189,0.01179218,0.0393857,0.004738078,0.00004146232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009620927,0.0005196299,0.9864339,0.000278518,0.00005452573,0.0001021198,0.0004533269,0.001610545,0.0009264835],"genre_scores_gemma":[0.12323,0.0006687272,0.8722718,0.0001183187,0.00005108808,0.0003923589,0.00158159,0.0003061493,0.001379867],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004828113,"threshold_uncertainty_score":0.01615167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05006105434218325,"score_gpt":0.2773288432822142,"score_spread":0.227267788940031,"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."}}