ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method
Aging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new aging-detecting marker for...
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| Format: | Article |
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MDPI AG
2023-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/1/34 |
| _version_ | 1797344612035067904 |
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| author | Lijuan Shi Boquan Hai Zhejun Kuang Han Wang Jian Zhao |
| author_facet | Lijuan Shi Boquan Hai Zhejun Kuang Han Wang Jian Zhao |
| author_sort | Lijuan Shi |
| collection | DOAJ |
| description | Aging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new aging-detecting marker for clinical diagnosis and treatments. Predicting the biological age of human individuals is conducive to the study of physical aging problems. Although many researchers have developed epigenetic clock prediction methods based on traditional machine learning and even deep learning, higher prediction accuracy is still required to match the clinical applications. Here, we proposed an epigenetic clock prediction method based on a Resnet neuro networks model named ResnetAge. The model accepts 22,278 CpG sites as a sample input, supporting both the Illumina 27K and 450K identification frameworks. It was trained using 32 public datasets containing multiple tissues such as whole blood, saliva, and mouth. The Mean Absolute Error (MAE) of the training set is 1.29 years, and the Median Absolute Deviation (MAD) is 0.98 years. The Mean Absolute Error (MAE) of the validation set is 3.24 years, and the Median Absolute Deviation (MAD) is 2.3 years. Our method has higher accuracy in age prediction in comparison with other methylation-based age prediction methods. |
| first_indexed | 2024-03-08T11:05:14Z |
| format | Article |
| id | doaj.art-56f4612c7d1d4881920aeea641246dbe |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-08T11:05:14Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-56f4612c7d1d4881920aeea641246dbe2024-01-26T15:06:12ZengMDPI AGBioengineering2306-53542023-12-011113410.3390/bioengineering11010034ResnetAge: A Resnet-Based DNA Methylation Age Prediction MethodLijuan Shi0Boquan Hai1Zhejun Kuang2Han Wang3Jian Zhao4Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, ChinaKey Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, ChinaKey Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, ChinaThe Institution of Computational Biology of Northeast Normal University, Changchun 130000, ChinaKey Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled (Changchun University), Ministry of Education, Changchun University, Changchun 130012, ChinaAging is a significant contributing factor to degenerative diseases such as cancer. The extent of DNA methylation in human cells indicates the aging process and screening for age-related methylation sites can be used to construct epigenetic clocks. Thereby, it can be a new aging-detecting marker for clinical diagnosis and treatments. Predicting the biological age of human individuals is conducive to the study of physical aging problems. Although many researchers have developed epigenetic clock prediction methods based on traditional machine learning and even deep learning, higher prediction accuracy is still required to match the clinical applications. Here, we proposed an epigenetic clock prediction method based on a Resnet neuro networks model named ResnetAge. The model accepts 22,278 CpG sites as a sample input, supporting both the Illumina 27K and 450K identification frameworks. It was trained using 32 public datasets containing multiple tissues such as whole blood, saliva, and mouth. The Mean Absolute Error (MAE) of the training set is 1.29 years, and the Median Absolute Deviation (MAD) is 0.98 years. The Mean Absolute Error (MAE) of the validation set is 3.24 years, and the Median Absolute Deviation (MAD) is 2.3 years. Our method has higher accuracy in age prediction in comparison with other methylation-based age prediction methods.https://www.mdpi.com/2306-5354/11/1/34DNA methylationCpG sitesage predictiondeep learning |
| spellingShingle | Lijuan Shi Boquan Hai Zhejun Kuang Han Wang Jian Zhao ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method Bioengineering DNA methylation CpG sites age prediction deep learning |
| title | ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method |
| title_full | ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method |
| title_fullStr | ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method |
| title_full_unstemmed | ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method |
| title_short | ResnetAge: A Resnet-Based DNA Methylation Age Prediction Method |
| title_sort | resnetage a resnet based dna methylation age prediction method |
| topic | DNA methylation CpG sites age prediction deep learning |
| url | https://www.mdpi.com/2306-5354/11/1/34 |
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