Year: 2024
Type: review
Abstract: Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age. Machine learning technologies offer promising avenues to enhance our understanding of the biological mechanisms governing aging by further elucidating the gap between biological and chronological ages. This perspective article examines current algorithmic approaches to epigenetic clocks, explores the use of machine learning for age estimation from DNA methylation, and discusses how refining the interpretation of ML methods and tailoring their inferences for specific patient populations and cell types can amplify the utility of these technologies in age prediction. By harnessing insights from machine learning, we are well-positioned to effectively adapt, customize and personalize interventions aimed at aging.
Source: Epigenomics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Center for Molecular Epidemiology, G6079320949, Cancer Center Support Grant, Biomedical National Elemental Imaging Resource (BNEIR), Center for Quantitative Biology: A focus on "omics", from organisms to single cells, A Multiplex Protein Biomarker-Based Immunoassay for the Early Detection of Bladder Cancer and its Implications in Tumor Biology
OpenAlex ID: W4404696426