Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods Article Swipe
Xiaoye Jin
,
Zheng Ren
,
Hongling Zhang
,
Qiyan Wang
,
Yubo Liu
,
Jingyan Ji
,
Jiang Huang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fgene.2022.924408
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fgene.2022.924408
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.
Related Topics
Concepts
Random forest
Biology
Computational biology
Inference
Support vector machine
Biological age
Machine learning
Messenger RNA
Gene expression
Expression (computer science)
Selection (genetic algorithm)
Ageing
Bioinformatics
Artificial intelligence
Genetics
Evolutionary biology
Computer science
Gene
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fgene.2022.924408
- OA Status
- gold
- Cited By
- 5
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283757466
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283757466Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fgene.2022.924408Digital Object Identifier
- Title
-
Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning MethodsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-07-01Full publication date if available
- Authors
-
Xiaoye Jin, Zheng Ren, Hongling Zhang, Qiyan Wang, Yubo Liu, Jingyan Ji, Jiang HuangList of authors in order
- Landing page
-
https://doi.org/10.3389/fgene.2022.924408Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fgene.2022.924408Direct OA link when available
- Concepts
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Random forest, Biology, Computational biology, Inference, Support vector machine, Biological age, Machine learning, Messenger RNA, Gene expression, Expression (computer science), Selection (genetic algorithm), Ageing, Bioinformatics, Artificial intelligence, Genetics, Evolutionary biology, Computer science, Gene, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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