{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003976751,0.00007359002,0.00009347355,0.00002179357,0.00003792953,0.000006154354,0.00006918354,0.00008554173,0.000005401091],"category_scores_gemma":[0.00005989899,0.00006512579,0.00002274438,0.00001786913,0.00007396405,0.000003413874,0.0001268644,0.00008865343,0.000002651597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001960662,"about_ca_system_score_gemma":0.00001298054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003794261,"about_ca_topic_score_gemma":0.00001073926,"domain_scores_codex":[0.9995798,0.00001757912,0.0001381086,0.0001422665,0.00003517483,0.00008707926],"domain_scores_gemma":[0.9996445,0.00009476527,0.00008107933,0.0001026683,0.00004155237,0.00003549111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006003079,0.0000528405,0.0005383531,0.0000118287,0.00002185654,4.007855e-7,0.00005021806,0.01103516,0.9832099,0.0000753099,0.00008290962,0.004861185],"study_design_scores_gemma":[0.0006695805,0.0002582649,0.0004890059,0.00001131154,0.00001252219,0.00004170862,0.00003034214,0.8074074,0.1864882,0.001810097,0.002610094,0.000171494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551239,0.00002838478,0.04441408,0.00002662375,0.0001222744,0.00007630017,0.000152559,0.000005194037,0.00005070114],"genre_scores_gemma":[0.977273,0.000006562413,0.02083597,0.00006670767,0.0001715492,0.000003889267,0.001632315,0.000005743095,0.000004288628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7967217,"threshold_uncertainty_score":0.2655753,"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."}}