{"id":"W2168980979","doi":"10.1093/bioinformatics/btq498","title":"Model-based clustering of microarray expression data via latent Gaussian mixture models","year":2010,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":226,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixture model; Bayesian information criterion; Cluster analysis; Covariance; Expectation–maximization algorithm; Computer science; Model selection; Gene chip analysis; Data mining; Gaussian; Statistical model; Bayesian probability; Artificial intelligence; Mathematics; Statistics; DNA microarray; Gene expression; Biology; Gene; Genetics; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005221775,0.001340169,0.001908002,0.00218399,0.0006772004,0.001582358,0.002320992,0.001499818,0.001395689],"category_scores_gemma":[0.01220773,0.0008048644,0.001947457,0.003973768,0.001198261,0.001996074,0.00149755,0.00198666,0.00163961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001337463,"about_ca_system_score_gemma":0.001464452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005352209,"about_ca_topic_score_gemma":0.003876729,"domain_scores_codex":[0.9963771,0.001559985,0.0001713608,0.0007931546,0.0009599967,0.000138492],"domain_scores_gemma":[0.9958866,0.002558795,0.0003943622,0.0005001835,0.0005895766,0.00007043288],"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":[0.0001931857,0.0001012006,0.004048637,0.0005312556,0.0002667212,0.0001194597,0.0003842173,0.7398279,0.008474026,0.03887624,0.004249649,0.2029274],"study_design_scores_gemma":[0.0000131272,0.0000249608,0.001072658,0.00002928738,0.00003455353,0.00007854966,0.00001997219,0.967033,0.001678005,0.02824108,0.001738738,0.00003593684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003588445,0.0003341764,0.9950705,0.0001264549,0.00001254663,0.00003738274,0.0001327413,0.0004642743,0.0002335218],"genre_scores_gemma":[0.1836413,0.00170011,0.8088726,0.0002001927,0.0001436001,0.0005765536,0.002671791,0.0002992567,0.001894606],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005352209,"threshold_uncertainty_score":0.02761567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03102465879333444,"score_gpt":0.266033582338828,"score_spread":0.2350089235454936,"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."}}