A multi-organization epigenetic age prediction based on a channel attention perceptron networks

Zhao, Jian and Li, Haixia and Qu, Jing and Zong, Xizeng and Liu, Yuchen and Kuang, Zhejun and Wang, Han (2024) A multi-organization epigenetic age prediction based on a channel attention perceptron networks. Frontiers in Genetics, 15. ISSN 1664-8021

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Abstract

DNA methylation indicates the individual’s aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.

Item Type: Article
Subjects: Eprints STM archive > Medical Science
Depositing User: Unnamed user with email admin@eprints.stmarchive
Date Deposited: 24 Apr 2024 08:19
Last Modified: 24 Apr 2024 08:19
URI: http://public.paper4promo.com/id/eprint/1957

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