{"id":"W2333987421","doi":"10.4018/ijcmam.2014070101","title":"Subspace Clustering of DNA Microarray Data","year":2014,"lang":"en","type":"article","venue":"International Journal of Computational Models and Algorithms in Medicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Biclustering; Cluster analysis; Computer science; Subspace topology; Identification (biology); Data mining; Microarray analysis techniques; Biological data; Gene chip analysis; Computational biology; DNA microarray; Artificial intelligence; Bioinformatics; Correlation clustering; Gene; Biology; CURE data clustering algorithm; 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":[0.0005031255,0.0000661806,0.0001410971,0.0001336201,0.00001256528,0.000007621974,0.0003304058,0.00003805046,0.000007482908],"category_scores_gemma":[0.00009242867,0.00005375505,0.0000210516,0.00004757435,0.00007359945,0.00001737093,0.00009906066,0.00006745903,1.314353e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009502499,"about_ca_system_score_gemma":0.0000482476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009639503,"about_ca_topic_score_gemma":0.000005062761,"domain_scores_codex":[0.999087,0.00004459735,0.0003689987,0.0001350078,0.0003078656,0.00005655555],"domain_scores_gemma":[0.9992073,0.0000399407,0.0002644327,0.0001111472,0.0003341667,0.00004299764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006266845,0.000259922,0.002395314,0.00006249066,0.0002772066,0.00001237632,0.0008032285,0.5083669,0.2575116,0.003300134,0.007136269,0.2192478],"study_design_scores_gemma":[0.003678121,0.0004167832,0.007783181,0.0004078439,0.00002204164,0.0002021825,0.0003133664,0.9517729,0.005138349,0.01707692,0.01302193,0.0001664017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1386467,0.0008339192,0.8571782,0.002487536,0.0004885399,0.00003718555,0.000009590374,0.00000128377,0.0003170803],"genre_scores_gemma":[0.9805828,0.0003828274,0.01817696,0.0002396983,0.0005027212,7.096828e-7,0.00007121666,0.000005962585,0.00003713489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8419361,"threshold_uncertainty_score":0.2192067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03978173201488262,"score_gpt":0.3340202196501705,"score_spread":0.2942384876352879,"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."}}