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

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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.

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          Most cited references36

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          NCBI GEO: archive for functional genomics data sets—update

          The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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            Regularization and variable selection via the elastic net

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              DNA methylation age of human tissues and cell types

              Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/1696795/overviewRole: Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/2665372/overviewRole: Role:
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                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                24 April 2024
                2024
                : 15
                : 1393856
                Affiliations
                [1] 1School of Computer Science and Technology, Changchun University , Changchun, China
                [2] 2 School of Computer Science and Technology , Jilin University , Changchun, China
                [3] 3 School of Information Science and Technology , Institute of Computational Biology , Northeast Normal University , Changchun, China
                [4] 4 Clinical Research Centre , Guangzhou First People’s Hospital , School of Medicine , South China University of Technology , Guangzhou, Guangdong, China
                [5] 5 School of Computer Science and Engineering , Changchun University of Technology , Changchun, China
                [6] 6 Department of Medicine , Boston University School of Medicine , Boston, MA, United States
                Author notes

                Edited by: Sigrid Le Clerc, Conservatoire National des Arts et Métiers (CNAM), France

                Reviewed by: Ernesto Borrayo, University of Guadalajara, Mexico

                Shixiang Sun, Albert Einstein College of Medicine, United States

                *Correspondence: Zhejun Kuang, kuangzhejun@ 123456ccu.edu.cn ; Han Wang, wangh101@ 123456nenu.edu.cn
                Article
                1393856
                10.3389/fgene.2024.1393856
                11080615
                38725481
                179d6873-6ef2-4a4e-ab0b-8c7a561b7300
                Copyright © 2024 Zhao, Li, Qu, Zong, Liu, Kuang and Wang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 March 2024
                : 09 April 2024
                Funding
                Funded by: Jilin Provincial Scientific and Technological Development Program , doi 10.13039/501100013061;
                Award ID: 20230201090GX 20230401092YY 20210101175JC YDZJ202303CGZH010 20210101477JC
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was partially supported by the National Natural Science Foundation of China under Grants No. 62372099, Jilin Scientific and Technological Development Program (Nos 20230201090GX, 20230401092YY, 20210101175JC, YDZJ202303CGZH010, and 20210101477JC), Capital Construction Funds within the Jilin Province budget (grant 2022C043-2).
                Categories
                Genetics
                Original Research
                Custom metadata
                Computational Genomics

                Genetics
                dna methylation,epigenetic clock,deep learning,attention mechanism,age prediction
                Genetics
                dna methylation, epigenetic clock, deep learning, attention mechanism, age prediction

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