Discrimination of Normal from Slow-Aging Mice by Plasma Metabolomic and Proteomic Features
Bretton Badenoch, Oliver Fiehn, Noa Rappaport, Pranjal Srivastava, Kengo Watanabe, Sriram Chandrasekaran, Richard A. Miller
The paper uses machine learning to identify plasma metabolomic and proteomic features that distinguish slow-aging mice from controls, finding triglycerides with varying fatty acid chain lengths as key predictors.
GeroScience · Dec 15, 2025Peer-reviewedBioinformatics