{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004480314,0.0002268381,0.0002281411,0.00003548631,0.000234391,0.0002022158,0.0001925111,0.0001304721,0.000002257543],"category_scores_gemma":[0.0001313908,0.0001771892,0.00009975866,0.00007788357,0.0002025948,0.00003591803,0.000121916,0.00008029548,0.000005737058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009059995,"about_ca_system_score_gemma":0.00007394349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001783418,"about_ca_topic_score_gemma":0.000008595463,"domain_scores_codex":[0.9986241,0.00002684195,0.0006145883,0.0002187655,0.0001424709,0.0003732028],"domain_scores_gemma":[0.9989158,0.00009234259,0.0002583046,0.000411096,0.0001019976,0.0002204636],"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":[0.01004862,0.00105358,0.04451489,0.004720066,0.002767352,0.000009952755,0.002677451,0.05571184,0.01413935,0.1139614,0.0547349,0.6956607],"study_design_scores_gemma":[0.005011086,0.001546176,0.03840486,0.0003538423,0.000476312,0.00002794025,0.0006722561,0.8904512,0.0003630862,0.01499344,0.04593661,0.001763185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1114063,0.0001718765,0.8867021,0.0001634731,0.0001657915,0.0005437874,0.0001135727,0.0000260042,0.0007070965],"genre_scores_gemma":[0.846375,0.0001500914,0.1511424,0.0008783807,0.0003766562,0.00006709206,0.0007248399,0.00003711046,0.0002483684],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8347394,"threshold_uncertainty_score":0.7225567,"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."}}