{"id":"W3188405687","doi":"10.1016/j.ymeth.2021.08.001","title":"NetAUC: A network-based multi-biomarker identification method by AUC optimization","year":2021,"lang":"en","type":"article","venue":"Methods","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"Science and Technology Program of Gansu Province; Training Program for Excellent Young Innovators of Changsha; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Interpretability; Receiver operating characteristic; Biomarker; Identification (biology); Computer science; Classifier (UML); Area under curve; Binary classification; Biological network; Computational biology; Machine learning; Biomarker discovery; Artificial intelligence; Bioinformatics; Support vector machine; Biology; Proteomics; Gene","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":[0.001537759,0.0001645174,0.0001754881,0.00002321111,0.0001187471,0.00007638217,0.0001586726,0.0002426362,0.00009834576],"category_scores_gemma":[0.0001319697,0.0001684838,0.0001219919,0.0002110636,0.000035299,0.000005633886,0.00008980663,0.00009900505,0.000006897355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001690993,"about_ca_system_score_gemma":0.0000905194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005804075,"about_ca_topic_score_gemma":0.000005966602,"domain_scores_codex":[0.9983469,0.0005588295,0.0003762934,0.0003476465,0.00009360851,0.0002766702],"domain_scores_gemma":[0.9990509,0.00005378138,0.0001669636,0.0004904163,0.0001501492,0.00008783137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005782624,0.0001152969,0.0002030248,0.00003910755,0.0001443073,0.000002146782,0.00003798983,0.3037855,0.4310479,0.00009764777,0.03866583,0.2258034],"study_design_scores_gemma":[0.0006233294,0.00002932505,0.0002415554,0.000009615458,0.00004855516,0.000009187943,0.00002020754,0.7560918,0.1247901,0.000121345,0.1177605,0.0002544617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002929778,0.002120411,0.9960982,0.0002506619,0.0004094875,0.0001530441,0.00001830093,0.00002074432,0.0006361433],"genre_scores_gemma":[0.004049663,0.0001129135,0.9912991,0.001026062,0.0001628239,0.00003276895,0.001157921,0.00002946768,0.002129289],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4523063,"threshold_uncertainty_score":0.6870572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02272812069918315,"score_gpt":0.3529032148169889,"score_spread":0.3301750941178058,"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."}}