{"id":"W2112492259","doi":"10.1093/bioinformatics/btu172","title":"HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery","year":2014,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Research Resources; National Institute of General Medical Sciences; National Institutes of Health","keywords":"Personalized medicine; Biomarker discovery; Computational biology; Biomarker; Genomics; Disease; Phenotype; Computer science; Bioinformatics; Biology; Gene; Genetics; Medicine; Genome; Proteomics","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":[0.001555394,0.0019323,0.0008351018,0.004476947,0.0006697251,0.001578696,0.001454641,0.0006525848,0.03253939],"category_scores_gemma":[0.006281527,0.0006273985,0.001681962,0.001665927,0.0003672489,0.001533421,0.002016766,0.0008488141,0.005431895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005651841,"about_ca_system_score_gemma":0.001392609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001545077,"about_ca_topic_score_gemma":0.003249713,"domain_scores_codex":[0.9994645,0.0001249317,0.00002818452,0.0001963178,0.0001396274,0.00004640314],"domain_scores_gemma":[0.9984029,0.0008809684,0.0002184215,0.0001510202,0.0001643434,0.0001823833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00258294,0.0005159688,0.08588734,0.0053565,0.00299016,0.001364024,0.0008382944,0.05829192,0.05967586,0.02414939,0.4094469,0.3489007],"study_design_scores_gemma":[0.001086897,0.0007289106,0.0583352,0.000905785,0.001723895,0.002422899,0.0003730437,0.6035686,0.06363265,0.133253,0.1335985,0.0003706134],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1532992,0.002753963,0.5104623,0.002756833,0.0006668209,0.001309577,0.199302,0.1141055,0.01534366],"genre_scores_gemma":[0.4258866,0.001884208,0.3838562,0.0006242548,0.0003646945,0.00237196,0.1660476,0.008578989,0.01038544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03253939,"threshold_uncertainty_score":0.1088551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645203915611146,"score_gpt":0.2520578626700897,"score_spread":0.2356058235139783,"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."}}