{"id":"W2135899964","doi":"10.1142/s0219720014500255","title":"Evaluating predictive performance of network biomarkers with network structures","year":2014,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Social connectedness; Computer science; Data mining; Graph; Weighted network; Network performance; Network analysis; Support vector machine; Network structure; Gene regulatory network; Artificial intelligence; Machine learning; Complex network; Theoretical computer science; Biology; Gene; Gene expression","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":[0.0007940552,0.000144387,0.000277661,0.00005521968,0.0001006844,0.00001821568,0.0001520492,0.0001247884,0.000004557173],"category_scores_gemma":[0.0000357512,0.0001015429,0.00006500019,0.0001047729,0.000188807,0.0000151701,0.00007863705,0.0001215808,4.277609e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007463135,"about_ca_system_score_gemma":0.0001081593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001027345,"about_ca_topic_score_gemma":0.000001256131,"domain_scores_codex":[0.9988058,0.00005533114,0.00069291,0.00008312605,0.0001504631,0.0002123433],"domain_scores_gemma":[0.9985088,0.00008993074,0.0008949994,0.0001057441,0.0003221997,0.00007836559],"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":[0.0009883912,0.00002351454,0.01593956,0.0001174312,0.0005683114,3.018198e-7,0.0001592387,0.904366,0.000449861,0.006254343,0.00133424,0.06979881],"study_design_scores_gemma":[0.002071037,0.008914792,0.02262192,0.0001482689,0.0001082625,0.000309737,0.0001289122,0.9438366,0.0002205086,0.01884383,0.002492124,0.0003040437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7851605,0.0005232207,0.2131497,0.00006599961,0.0002158801,0.0001228888,0.00001084032,0.00000325065,0.0007476953],"genre_scores_gemma":[0.872451,0.0000949492,0.1267839,0.0002168814,0.0003878099,0.000001275196,0.0000518517,0.000007361443,0.000004977303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08729052,"threshold_uncertainty_score":0.4140801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008379428439424798,"score_gpt":0.2511448919736238,"score_spread":0.242765463534199,"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."}}