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Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods

Aging is usually accompanied by the decline of physiological function and dysfunction of cellular processes. Genetic markers related to aging not only reveal the biological mechanism of aging but also provide age information in forensic research. In this study, we aimed to screen age-associated mRNA...

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Autores principales: Jin, Xiaoye, Ren, Zheng, Zhang, Hongling, Wang, Qiyan, Liu, Yubo, Ji, Jingyan, Huang, Jiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283997/
https://www.ncbi.nlm.nih.gov/pubmed/35846135
http://dx.doi.org/10.3389/fgene.2022.924408
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author Jin, Xiaoye
Ren, Zheng
Zhang, Hongling
Wang, Qiyan
Liu, Yubo
Ji, Jingyan
Huang, Jiang
author_facet Jin, Xiaoye
Ren, Zheng
Zhang, Hongling
Wang, Qiyan
Liu, Yubo
Ji, Jingyan
Huang, Jiang
author_sort Jin, Xiaoye
collection PubMed
description Aging is usually accompanied by the decline of physiological function and dysfunction of cellular processes. Genetic markers related to aging not only reveal the biological mechanism of aging but also provide age information in forensic research. In this study, we aimed to screen age-associated mRNAs based on the previously reported genome-wide expression data. In addition, predicted models for age estimations were built by three machine learning methods. We identified 283 differentially expressed mRNAs between two groups with different age ranges. Nine mRNAs out of 283 mRNAs showed different expression patterns between smokers and non-smokers and were eliminated from the following analysis. Age-associated mRNAs were further screened from the remaining mRNAs by the cross-validation error analysis of random forest. Finally, 14 mRNAs were chosen to build the model for age predictions. These 14 mRNAs showed relatively high correlations with age. Furthermore, we found that random forest showed the optimal performance for age prediction in comparison to the generalized linear model and support vector machine. To sum up, the 14 age-associated mRNAs identified in this study could be viewed as valuable markers for age estimations and studying the aging process.
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spelling pubmed-92839972022-07-16 Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods Jin, Xiaoye Ren, Zheng Zhang, Hongling Wang, Qiyan Liu, Yubo Ji, Jingyan Huang, Jiang Front Genet Genetics Aging is usually accompanied by the decline of physiological function and dysfunction of cellular processes. Genetic markers related to aging not only reveal the biological mechanism of aging but also provide age information in forensic research. In this study, we aimed to screen age-associated mRNAs based on the previously reported genome-wide expression data. In addition, predicted models for age estimations were built by three machine learning methods. We identified 283 differentially expressed mRNAs between two groups with different age ranges. Nine mRNAs out of 283 mRNAs showed different expression patterns between smokers and non-smokers and were eliminated from the following analysis. Age-associated mRNAs were further screened from the remaining mRNAs by the cross-validation error analysis of random forest. Finally, 14 mRNAs were chosen to build the model for age predictions. These 14 mRNAs showed relatively high correlations with age. Furthermore, we found that random forest showed the optimal performance for age prediction in comparison to the generalized linear model and support vector machine. To sum up, the 14 age-associated mRNAs identified in this study could be viewed as valuable markers for age estimations and studying the aging process. Frontiers Media S.A. 2022-07-01 /pmc/articles/PMC9283997/ /pubmed/35846135 http://dx.doi.org/10.3389/fgene.2022.924408 Text en Copyright © 2022 Jin, Ren, Zhang, Wang, Liu, Ji and Huang. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Genetics
Jin, Xiaoye
Ren, Zheng
Zhang, Hongling
Wang, Qiyan
Liu, Yubo
Ji, Jingyan
Huang, Jiang
Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods
title Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods
title_full Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods
title_fullStr Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods
title_full_unstemmed Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods
title_short Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods
title_sort systematic selection of age-associated mrna markers and the development of predicted models for forensic age inference by three machine learning methods
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283997/
https://www.ncbi.nlm.nih.gov/pubmed/35846135
http://dx.doi.org/10.3389/fgene.2022.924408
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