{"id":"W2794835735","doi":"10.1101/289934","title":"Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery","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":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Cure Brain Cancer Foundation; National Cancer Institute; Ontario Institute for Cancer Research; Cancer Research UK; BC Cancer Agency; Government of Ontario; National Human Genome Research Institute; University of Toronto","keywords":"Bespoke; Subnetwork; Computer science; Computational biology; Precision medicine; Biomarker discovery; Biological network; Data mining; Data science; Biology; Gene; 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":[0.002426193,0.001416893,0.0009571891,0.00250803,0.0003733247,0.001573519,0.001000031,0.0009176732,0.003065099],"category_scores_gemma":[0.008072546,0.00053189,0.001264509,0.001438858,0.0005459004,0.001752181,0.001771603,0.001529196,0.0009753323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009461295,"about_ca_system_score_gemma":0.0008337708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001629791,"about_ca_topic_score_gemma":0.002366037,"domain_scores_codex":[0.9990348,0.0004200469,0.00005449952,0.0002912314,0.0001509941,0.00004839029],"domain_scores_gemma":[0.9967006,0.001842417,0.0005474674,0.0004528564,0.0002982564,0.0001583922],"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":[0.0009336583,0.0003079511,0.06399505,0.000466074,0.001339567,0.0004409447,0.0001624183,0.7195634,0.01639406,0.02648073,0.00958357,0.1603325],"study_design_scores_gemma":[0.00002651537,0.000073622,0.003620165,0.00003577546,0.00007404221,0.00006057106,0.00001906782,0.9410545,0.002503423,0.05001038,0.002505792,0.00001619709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1519316,0.002188142,0.8293273,0.002621852,0.0002153656,0.0001584665,0.005176041,0.004983296,0.003397859],"genre_scores_gemma":[0.7843767,0.0008967413,0.2067685,0.0003278554,0.0001458572,0.00015759,0.005437645,0.0002862859,0.001602867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003065099,"threshold_uncertainty_score":0.01283109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01138798076142466,"score_gpt":0.2265945062414249,"score_spread":0.2152065254800002,"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."}}