{"id":"W1979371339","doi":"10.1371/journal.pcbi.1000742","title":"Simultaneous Clustering of Multiple Gene Expression and Physical Interaction Datasets","year":2010,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cluster analysis; Heuristic; Systems biology; Computer science; Computational biology; Flexibility (engineering); Gene regulatory network; Biological network; Data mining; Gene; Expression (computer science); Biology; Artificial intelligence; Gene expression; Genetics; Mathematics; Statistics","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":[0.002561077,0.001198521,0.001334671,0.004773124,0.001019292,0.001558934,0.00196179,0.001599703,0.0008874002],"category_scores_gemma":[0.008160786,0.0005638043,0.001762913,0.006249054,0.0006037448,0.0009807252,0.002437048,0.001775481,0.0006220594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296617,"about_ca_system_score_gemma":0.001386847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004018327,"about_ca_topic_score_gemma":0.007337513,"domain_scores_codex":[0.996067,0.0009783044,0.000219287,0.001609854,0.0008973699,0.0002282931],"domain_scores_gemma":[0.9957867,0.001659647,0.0004624846,0.001097951,0.0008280873,0.0001651248],"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":[0.002011993,0.0008994802,0.1126237,0.002111857,0.002631768,0.001223426,0.0007744381,0.3981279,0.2070668,0.02075132,0.02295629,0.2288211],"study_design_scores_gemma":[0.0001556588,0.0002390046,0.08605587,0.00007670181,0.0005082479,0.0005270605,0.0005209598,0.7735459,0.06225256,0.0425537,0.03339208,0.0001722862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3263916,0.002311632,0.6186845,0.001019896,0.0001832994,0.0005841856,0.04178657,0.004912255,0.004126095],"genre_scores_gemma":[0.5062063,0.0007678273,0.3874952,0.0004792259,0.0000947459,0.001300025,0.1011612,0.0003801749,0.002115305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004773124,"threshold_uncertainty_score":0.01354444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009639149645694999,"score_gpt":0.2562097429824766,"score_spread":0.2465705933367816,"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."}}