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A Comprehensive Assessment of Methylation-Based Age Prediction Methods

Wang, H.; Qu, J.; Zong, Z. X.; Cai, R. R.; Li, K. Y.; Zhao, L. S.; Hai, Q. B.; Tian, T. R.; Kuang, J. Z.; Zhang, L.; Zhao, J.; Liu, X. G.; Zhang, C.

2024-12-23 bioinformatics
10.1101/2024.12.23.627443 bioRxiv
Show abstract

DNA methylation (DNAm) clock is widely used to measure biological age, helping to identify key biomarkers associated with aging, infer the progression of aging, and have promise for elucidating, delaying, or even reversing aging. During the past decade, a large number of epigenetic clocks have been developed. However, they are decentralized, with applicable scopes overlapping. We benchmark 15 of these methods on 142 Illumina DNAm array datasets in five criteria and analyze the biological significance of CPGs about aging and overlapping. There are many exciting commons in models performance. We found the optimal model closely related to the numbers and characteristics of the training data. We provided a comprehensive assessment process to guide DNAm clock research at (https://dnamclock.com), the corresponding data and evaluation pipeline are freely available (https://github.comyNENUBioCompute/MethylationEvaluation), this study will aid in the development of improved tools designed to analyze increasingly large DNAm datasets.

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The top 4 journals account for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.