{"id":"W1975341743","doi":"10.1504/ijdmb.2010.035898","title":"Using Gene Ontology to enhance effectiveness of similarity measures for microarray data","year":2010,"lang":"en","type":"article","venue":"International Journal of Data Mining and Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Redundancy (engineering); Gene ontology; Computer science; Semantic similarity; Data mining; Feature selection; Similarity (geometry); Gene selection; Microarray analysis techniques; Artificial intelligence; Pattern recognition (psychology); Gene; Gene expression; Biology; Genetics","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":[0.0009433402,0.00006746373,0.0001224298,0.0000755644,0.00002850235,0.00002826679,0.0008581082,0.00007153614,0.000001685861],"category_scores_gemma":[0.0005312133,0.00005622169,0.00002261604,0.00003165196,0.00004401616,0.00004243381,0.0003364496,0.00006105265,1.218812e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005796529,"about_ca_system_score_gemma":0.0001279086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004639323,"about_ca_topic_score_gemma":0.00002239467,"domain_scores_codex":[0.9992855,0.00002701163,0.0003414715,0.0001204088,0.0001531289,0.00007252433],"domain_scores_gemma":[0.99877,0.00005214543,0.0003238904,0.0004043559,0.0003980463,0.00005155183],"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":[0.0002709596,0.00002925351,0.0009541618,0.00003266445,0.00008471137,3.561696e-7,0.00007029805,0.000008988123,0.9349967,0.000006482236,0.001163368,0.06238208],"study_design_scores_gemma":[0.000797971,0.0002507297,0.001835458,0.0001491256,0.00005872175,0.000181429,0.0002348462,0.006988453,0.957881,0.00003495308,0.03143234,0.0001549472],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7165506,0.0001446617,0.281812,0.0001505946,0.0006494477,0.000083824,0.0005669753,0.000001288623,0.00004061244],"genre_scores_gemma":[0.6870803,0.00007288565,0.3121783,0.00008436623,0.0001860996,8.453645e-7,0.0003874568,0.000004356429,0.000005461984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06222713,"threshold_uncertainty_score":0.2292654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08225493591224538,"score_gpt":0.400972716185559,"score_spread":0.3187177802733136,"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."}}