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AuthorRabbani, Naila
Available date2023-10-11T09:46:47Z
Publication Date2022-04-21
Publication NameInternational Journal of Molecular Sciences
Identifierhttp://dx.doi.org/10.3390/ijms23094584
CitationRabbani, N. (2022). AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics. International Journal of Molecular Sciences, 23(9), 4584.
ISSN1661-6596
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85128382942&origin=inward
URIhttp://hdl.handle.net/10576/48446
AbstractProtein damage by glycation, oxidation and nitration is a continuous process in the physiological system caused by reactive metabolites associated with dicarbonyl stress, oxidative stress and nitrative stress, respectively. The term AGEomics is defined as multiplexed quantitation of spontaneous modification of proteins damage and other usually low-level modifications associated with a change of structure and function—for example, citrullination and transglutamination. The method of quantitation is stable isotopic dilution analysis liquid chromatography—tandem mass spec-trometry (LC-MS/MS). This provides robust quantitation of normal and damaged or modified amino acids concurrently. AGEomics biomarkers have been used in diagnostic algorithms using machine learning methods. In this review, I describe the utility of AGEomics biomarkers and provide evidence why these are close to the phenotype of a condition or disease compared to other metabolites and metabolomic approaches and how to train and test algorithms for clinical diagnostic and screening applications with high accuracy, sensitivity and specificity using machine learning approaches.
SponsorThis research was funded by Qatar University, grant number QUHI-CMED-21/22-1.
Languageen
PublisherMultidisciplinary Digital Publishing Institute (MDPI)
SubjectAGEomics
Alzheimer’s disease
arthritis
autism
diabetes
glycation
machine learning
Parkinson’s disease
TitleAGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics
TypeArticle
Issue Number9
Volume Number23
ESSN1422-0067
dc.accessType Open Access


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