{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008732194,0.0001070486,0.00008631058,0.00004856282,0.00009130995,0.00005734549,0.0002117001,0.0001033273,0.00001199276],"category_scores_gemma":[0.00003324722,0.000100057,0.00001822827,0.00008958488,0.00003778085,0.00002107059,0.0001994117,0.00004539562,0.000005727556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008242831,"about_ca_system_score_gemma":0.00001204615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001927664,"about_ca_topic_score_gemma":0.000002407686,"domain_scores_codex":[0.9993455,0.00001972612,0.0002167725,0.0001884503,0.00009666475,0.0001328471],"domain_scores_gemma":[0.9993109,0.000003288168,0.0001062755,0.0004939352,0.00002774847,0.00005786025],"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":[0.00003397347,0.00005284475,0.0005698298,0.0002115943,0.00003688774,3.588099e-7,0.00102146,0.00009775403,0.9317357,0.0001506391,0.01902458,0.0470644],"study_design_scores_gemma":[0.001745664,0.0002790259,0.007747676,0.00007336276,0.00006000183,0.00007912307,0.001494743,0.6790583,0.1160536,0.00002212611,0.1925734,0.0008129943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6877412,0.004588388,0.2931118,0.0004672514,0.0008183751,0.001004996,0.0001622613,0.0001408189,0.01196491],"genre_scores_gemma":[0.9672679,0.0005005395,0.03067993,0.0002227328,0.0001435459,0.000009614843,0.0008364529,0.00001760211,0.0003216681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8156821,"threshold_uncertainty_score":0.4080208,"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."}}