aceExplainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank
Osteoporosis (OP) is often underdiagnosed, highlighting the need for tools that can both detect existing disease and predict future risk; large-scale plasma proteomics combined with explainable machine learning enables integrated diagnostic and prognostic modeling while prioritizing clinically relevant protein markers. This study aims to develop and validate an explainable plasma proteomics machine-learning framework for osteoporosis diagnosis, future risk prediction, and biomarker discovery. We further tested w…
Plasma proteomics XGBoost models with SHAP-selected markers improved discrimination of prevalent osteoporosis and prediction of incident osteoporosis from baseline samples in UK Biobank.
Findings require further validation before clinical use as plasma protein biomarkers for osteoporosis risk stratification.
Evidence
- Peer-reviewedThe FASEB Journal2026-09-01
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Truvace Impact Record TRV-2026-0947, v1: “Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.” Truvace, 2026-09-01. /record/TRV-2026-0947 (accessed at citation time). sha256 f03b1f5a988c11cb…
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