{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004777026,0.002035852,0.002054752,0.001596305,0.0008615638,0.001344618,0.002865163,0.002525893,0.001493531],"category_scores_gemma":[0.01306885,0.0006260193,0.001551113,0.001564657,0.00145816,0.001631845,0.003908219,0.002583746,0.0006134973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008930529,"about_ca_system_score_gemma":0.001681242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002474939,"about_ca_topic_score_gemma":0.002040219,"domain_scores_codex":[0.9966719,0.00152729,0.0001738407,0.0008848842,0.0005275657,0.0002144984],"domain_scores_gemma":[0.9934454,0.004547799,0.000502435,0.0006794892,0.0005635676,0.0002612417],"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":[0.001037578,0.0004986114,0.01582713,0.0005141124,0.0007579135,0.0006696273,0.0005036035,0.5915337,0.009437501,0.01987459,0.01023539,0.3491103],"study_design_scores_gemma":[0.00002361147,0.00006826363,0.0006248373,0.00001300482,0.00003974805,0.00005769634,0.00002017109,0.9783156,0.001568122,0.01847335,0.0007754926,0.00002006093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03403771,0.0009818496,0.9608058,0.00102542,0.00006376894,0.0001007894,0.0004001771,0.001392233,0.001192316],"genre_scores_gemma":[0.7723629,0.0005884877,0.221591,0.0009214412,0.0002943604,0.0003955819,0.001371781,0.0001507399,0.002323692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004777026,"threshold_uncertainty_score":0.02526367,"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."}}