The abundance of the biological data and the rapid evolution of the newer machine learning technologies have increased the epigenetics research in the last decade. This has enhanced the ability to measure the biological age of humans and different organisms via their omics data. DNA methylation array data are commonly used in the prediction of methylation age. Horvath clock has been adopted in various aging studies as a DNA methylation age predicting clock due to its higher accuracy and multi tissue prediction potential. In the current study, we have developed a pan tissue methylation-aging clock by using the publicly available illumina 450k and EPIC array methylation datasets. In doing that, we developed a highly accurate epigenetic clock, which predicts the age of multiple tissues with higher accuracy. We have also analyzed the selected probes for their biological relevance. Upon analyzing the selected features further, we found out evidences, which support the Antagonistic pleiotropy theory of aging.
Keywords: Aging; Artificial intelligence; DNA methylation; Epigenetics; Machine learning.
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