Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1784747456940998656 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9283997 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT jinxiaoye systematicselectionofageassociatedmrnamarkersandthedevelopmentofpredictedmodelsforforensicageinferencebythreemachinelearningmethods AT renzheng systematicselectionofageassociatedmrnamarkersandthedevelopmentofpredictedmodelsforforensicageinferencebythreemachinelearningmethods AT zhanghongling systematicselectionofageassociatedmrnamarkersandthedevelopmentofpredictedmodelsforforensicageinferencebythreemachinelearningmethods AT wangqiyan systematicselectionofageassociatedmrnamarkersandthedevelopmentofpredictedmodelsforforensicageinferencebythreemachinelearningmethods AT liuyubo systematicselectionofageassociatedmrnamarkersandthedevelopmentofpredictedmodelsforforensicageinferencebythreemachinelearningmethods AT jijingyan systematicselectionofageassociatedmrnamarkersandthedevelopmentofpredictedmodelsforforensicageinferencebythreemachinelearningmethods AT huangjiang systematicselectionofageassociatedmrnamarkersandthedevelopmentofpredictedmodelsforforensicageinferencebythreemachinelearningmethods |