NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints
| العنوان: | NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints |
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| المؤلفون: | Dimitri, GM, Meoni, G, Tenori, L, Luchinat, C, Lió, P |
| المساهمون: | Dimitri, GM [0000-0002-2728-4272], Meoni, G [0000-0002-8608-4641], Tenori, L [0000-0001-6438-059X], Luchinat, C [0000-0003-2271-8921], Apollo - University of Cambridge Repository |
| المصدر: | Applied Sciences; Volume 12; Issue 18; Pages: 8954 |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Fluid Flow and Transfer Processes, metabolomics aging, Process Chemistry and Technology, Parkinson's disease, machine learning, spectrum, metabolites, lipids, Parkinson’s disease, biological age, General Engineering, Computer Science Applications, General Materials Science, Instrumentation |
| الوصف: | Peer reviewed: True 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. |
| وصف الملف: | application/zip; text/xml; application/pdf |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd7eb2fba0854a9b0666174ccc7b7bc7Test https://www.repository.cam.ac.uk/handle/1810/341715Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....dd7eb2fba0854a9b0666174ccc7b7bc7 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |