A multi-organization epigenetic age prediction based on a channel attention perceptron networks
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...
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Published in | Frontiers in genetics Vol. 15; p. 1393856 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
24.04.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Shixiang Sun, Albert Einstein College of Medicine, United States Reviewed by: Ernesto Borrayo, University of Guadalajara, Mexico Edited by: Sigrid Le Clerc, Conservatoire National des Arts et Métiers (CNAM), France |
ISSN: | 1664-8021 1664-8021 |
DOI: | 10.3389/fgene.2024.1393856 |