Abstract
Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three different and well-known machine learning approaches—SVM, ElasticNet, and PLS—to build a model based on the 1H-NMR metabolomic data of serum samples, able to predict chronological age in control individuals. Then, we tested these models in two pathological cohorts of de novo and advanced PD patients. The discrepancies observed between predicted and actual age in patients are interpreted as a sign of a (pathological) biological aging process.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
