{"id":"W2794644412","doi":"10.1101/290924","title":"Subnetwork-based prognostic biomarkers exhibit performance and robustness superior to gene-based biomarkers in breast cancer","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Government of Ontario; Terry Fox Research Institute; Ontario Institute for Cancer Research","keywords":"Subnetwork; Robustness (evolution); Biomarker; Biomarker discovery; Breast cancer; Concordance; Computational biology; Cohort; Biology; Bioinformatics; Computer science; Oncology; Cancer; Medicine; Gene; Internal medicine; Proteomics; Genetics","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":[0.006950249,0.0009632147,0.001109603,0.001964503,0.0003962921,0.001714387,0.0006629485,0.0008516338,0.001183771],"category_scores_gemma":[0.01850176,0.0003847492,0.001279323,0.00153885,0.0005912458,0.001064938,0.001151116,0.0009050205,0.0003448976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007666171,"about_ca_system_score_gemma":0.0005164396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001550377,"about_ca_topic_score_gemma":0.001946592,"domain_scores_codex":[0.9977378,0.001084949,0.0001610272,0.0006722508,0.0002283707,0.0001156304],"domain_scores_gemma":[0.9904039,0.00554708,0.001796945,0.001391452,0.0005786024,0.0002819574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002799986,0.0002692729,0.6891156,0.0006820477,0.004752759,0.0001729296,0.0001868999,0.1673602,0.02722188,0.001242893,0.003476026,0.1027195],"study_design_scores_gemma":[0.0002652904,0.001253062,0.4252866,0.000265013,0.002640054,0.0006506993,0.0002178007,0.5173118,0.02432253,0.02263324,0.005013503,0.0001404634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9511274,0.004248229,0.03679015,0.001075178,0.00009149518,0.0000541409,0.004624095,0.0008405464,0.001148745],"genre_scores_gemma":[0.9882981,0.0003474002,0.007978573,0.000114464,0.0000456296,0.00003044301,0.002969744,0.00004712071,0.000168452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006950249,"threshold_uncertainty_score":0.03675687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007640087747253257,"score_gpt":0.2057971853243445,"score_spread":0.1981570975770912,"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."}}