{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005302463,0.001255101,0.0007274218,0.003798261,0.0003599697,0.001027674,0.0007064409,0.001046973,0.0006667696],"category_scores_gemma":[0.02148792,0.0002390893,0.0006895478,0.002160414,0.0005072387,0.001441503,0.0008493752,0.0009020405,0.0001874055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008005324,"about_ca_system_score_gemma":0.0007282083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004842555,"about_ca_topic_score_gemma":0.004277693,"domain_scores_codex":[0.9985451,0.0006765284,0.00009045601,0.0003118366,0.0002794176,0.00009675109],"domain_scores_gemma":[0.984338,0.01255576,0.001295291,0.0008560937,0.000730327,0.0002245235],"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":[0.0005701771,0.0001873981,0.1054704,0.000147009,0.0004266167,0.0001502335,0.00007186029,0.8151634,0.002343405,0.001342583,0.001314758,0.072812],"study_design_scores_gemma":[0.00001280849,0.0001368597,0.008758043,0.00001579726,0.00005072925,0.00005516574,0.00002355375,0.9875901,0.001276069,0.001865262,0.0002041879,0.00001139055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.864374,0.001957419,0.1271426,0.0009318907,0.00008871304,0.0001496337,0.002308707,0.001066773,0.001980246],"genre_scores_gemma":[0.9763648,0.0003293641,0.02082393,0.00003894128,0.00005016049,0.0000668098,0.001947126,0.00002909913,0.0003497954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005302463,"threshold_uncertainty_score":0.02804244,"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."}}