{"id":"W4409609763","doi":"10.1093/bib/bbaf178","title":"Mutual-assistance learning for trustworthy biomarker discovery and disease prediction","year":2025,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; H. Lundbeck A/S; Servier; Eisai; National Institutes of Health; Northwestern Polytechnical University; Genentech; IXICO; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; Pfizer; Northwestern University; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Interpretability; Computer science; Machine learning; Identification (biology); Artificial intelligence; Feature (linguistics); Biomarker; Disease; Flexibility (engineering); Biomarker discovery; Data science; Medicine; Biology; Proteomics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002833823,0.000162838,0.0001559517,0.00009349816,0.0001391643,0.0001178611,0.0001123518,0.0001391137,0.00000128714],"category_scores_gemma":[0.0002084827,0.0001601757,0.00006842802,0.0001393159,0.00009541677,0.00003343084,0.0001225622,0.0001091134,0.000001114829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002349177,"about_ca_system_score_gemma":0.0001036245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005849342,"about_ca_topic_score_gemma":0.00001455066,"domain_scores_codex":[0.9990234,0.00001346285,0.00044601,0.0001708813,0.00007887166,0.0002673417],"domain_scores_gemma":[0.9994974,0.00003415482,0.0001331519,0.0002058077,0.00004311301,0.00008638991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004451134,0.0004535463,0.2040472,0.005447422,0.0007247681,0.000007945074,0.00225939,0.009526078,0.007318178,0.03945319,0.2328483,0.4934629],"study_design_scores_gemma":[0.003883566,0.0002424301,0.08014279,0.0004897546,0.0001033391,0.000009239627,0.0005310212,0.4677443,0.0006512569,0.005114474,0.4403607,0.0007270889],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3558492,0.002826114,0.6302162,0.00165503,0.0006404544,0.001362927,0.0001781482,0.00006822794,0.007203731],"genre_scores_gemma":[0.984154,0.0008440916,0.008847343,0.002729294,0.00009096986,0.00009953338,0.0005043354,0.00002235098,0.002708043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6283048,"threshold_uncertainty_score":0.6531774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006307100433656365,"score_gpt":0.2282079311886826,"score_spread":0.2219008307550262,"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."}}