{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399796,0.0006238436,0.00117872,0.003329484,0.0004641606,0.0009976892,0.0006409559,0.0004991395,0.001077646],"category_scores_gemma":[0.004542207,0.0002931025,0.0008616594,0.004212725,0.0005973684,0.00076164,0.000811639,0.0007614713,0.0007737277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006528921,"about_ca_system_score_gemma":0.001106024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002435167,"about_ca_topic_score_gemma":0.002119576,"domain_scores_codex":[0.997664,0.000796565,0.0001599615,0.000508989,0.0007154168,0.0001551785],"domain_scores_gemma":[0.9983096,0.0006707605,0.0001953365,0.0002566073,0.0004997737,0.00006785186],"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":[0.0006762694,0.0001217301,0.008521188,0.00141009,0.0004511335,0.0002685532,0.0004664537,0.3047876,0.0633648,0.03596748,0.01006667,0.5738981],"study_design_scores_gemma":[0.00002204698,0.0001358987,0.006790589,0.00005530244,0.00004823413,0.0003100843,0.0002440191,0.9074477,0.01491369,0.05706679,0.012875,0.00009072199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02979605,0.001562863,0.9641951,0.0002589806,0.00007167407,0.0001214334,0.001836332,0.001033928,0.00112373],"genre_scores_gemma":[0.3703677,0.003405856,0.611591,0.0002408595,0.0001374942,0.0007409871,0.01066046,0.0002015566,0.002654182],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003329484,"threshold_uncertainty_score":0.007402956,"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."}}