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A multi-organization epigenetic age prediction based on a channel attention perceptron networks
A multi-organization epigenetic age prediction based on a channel attention perceptron networks
Jian Zhao
(School of Computer Science and Technology, Changchun University, Changchun, China)
Haixia Li
(School of Computer Science and Technology, Changchun University, Changchun, China)
Jing Qu
(School of Computer Science and Technology, Jilin University, Changchun, China)
Jing Qu
(School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China)
Xizeng Zong
(Clinical Research Centre, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China)
Xizeng Zong
(School of Computer Science and Engineering, Changchun University of Technology, Changchun, China)
Yuchen Liu
(Department of Medicine, Boston University School of Medicine, Boston, MA, United States)
Zhejun Kuang
(School of Computer Science and Technology, Changchun University, Changchun, China)
Han Wang
(School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, China)
Journal: Frontiers in Genetics
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• Frontiers Media S.A.
• ISSN: 1664-8021
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.
Jian Zhao, Haixia Li, Jing Qu, et al. (2024). "A multi-organization epigenetic age prediction based on a channel attention perceptron networks". Frontiers in Genetics. https://doi.org/10.3389/fgene.2024.1393856