{"id":"W2157911533","doi":"10.1109/cbms.2006.100","title":"Incorporating Gene Ontology in Clustering Gene Expression Data","year":2006,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Data mining; Biological data; Computer science; Ontology; Gene ontology; Data integration; Expression (computer science); Measure (data warehouse); Artificial intelligence; Bioinformatics; Gene; Gene expression; Biology","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":[0.0001634364,0.000103143,0.00009553493,0.00005465641,0.00004686452,0.00001772075,0.0003196286,0.0001263836,0.00002252308],"category_scores_gemma":[0.00002182117,0.00009415452,0.00001864804,0.00008568964,0.00002702871,0.000006830533,0.0003593668,0.00005850656,0.000005477645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001377731,"about_ca_system_score_gemma":0.00004871059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001469902,"about_ca_topic_score_gemma":0.0008635495,"domain_scores_codex":[0.9990087,0.00005456401,0.0002396855,0.0004476439,0.00008443645,0.0001649785],"domain_scores_gemma":[0.9991524,0.000004443638,0.00008638945,0.0006963446,0.00002632691,0.0000341091],"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":[0.00002779657,0.00003556029,0.006376333,0.000004618536,0.000001798312,0.000002210738,0.00000463886,0.0002927786,0.9876755,0.00004122909,0.003660136,0.001877431],"study_design_scores_gemma":[0.0004909571,0.00003154312,0.008017534,0.00001088277,0.000002364406,0.00001145298,0.00003988427,0.001701975,0.974337,0.0001418449,0.01506494,0.0001496471],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8610455,0.0008435909,0.129377,0.0003195477,0.0002479915,0.0001934862,0.00001732635,0.00003051132,0.007925087],"genre_scores_gemma":[0.9690907,0.00003855495,0.02859438,0.0001742105,0.0002626695,0.00002001007,0.0008495321,0.00001405338,0.0009558646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1080453,"threshold_uncertainty_score":0.383951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02984900334608226,"score_gpt":0.2817890745918252,"score_spread":0.2519400712457429,"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."}}