Group-shrinkage feature selection with a spatial network for mining DNA methylation data
Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/170639 |
| _version_ | 1826112863911018496 |
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| author | Tang, Xinlu Mo, Zhanfeng Chang, Cheng Qian, Xiaohua |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Tang, Xinlu Mo, Zhanfeng Chang, Cheng Qian, Xiaohua |
| author_sort | Tang, Xinlu |
| collection | NTU |
| description | Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication. |
| first_indexed | 2024-10-01T03:14:04Z |
| format | Journal Article |
| id | ntu-10356/170639 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:14:04Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1706392023-09-25T02:36:47Z Group-shrinkage feature selection with a spatial network for mining DNA methylation data Tang, Xinlu Mo, Zhanfeng Chang, Cheng Qian, Xiaohua School of Computer Science and Engineering Engineering::Computer science and engineering DNA Methylation Feature Selection Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication. 2023-09-25T02:36:47Z 2023-09-25T02:36:47Z 2023 Journal Article Tang, X., Mo, Z., Chang, C. & Qian, X. (2023). Group-shrinkage feature selection with a spatial network for mining DNA methylation data. Computers in Biology and Medicine, 154, 106573-. https://dx.doi.org/10.1016/j.compbiomed.2023.106573 0010-4825 https://hdl.handle.net/10356/170639 10.1016/j.compbiomed.2023.106573 36706568 2-s2.0-85147326888 154 106573 en Computers in Biology and Medicine © 2023 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering DNA Methylation Feature Selection Tang, Xinlu Mo, Zhanfeng Chang, Cheng Qian, Xiaohua Group-shrinkage feature selection with a spatial network for mining DNA methylation data |
| title | Group-shrinkage feature selection with a spatial network for mining DNA methylation data |
| title_full | Group-shrinkage feature selection with a spatial network for mining DNA methylation data |
| title_fullStr | Group-shrinkage feature selection with a spatial network for mining DNA methylation data |
| title_full_unstemmed | Group-shrinkage feature selection with a spatial network for mining DNA methylation data |
| title_short | Group-shrinkage feature selection with a spatial network for mining DNA methylation data |
| title_sort | group shrinkage feature selection with a spatial network for mining dna methylation data |
| topic | Engineering::Computer science and engineering DNA Methylation Feature Selection |
| url | https://hdl.handle.net/10356/170639 |
| work_keys_str_mv | AT tangxinlu groupshrinkagefeatureselectionwithaspatialnetworkforminingdnamethylationdata AT mozhanfeng groupshrinkagefeatureselectionwithaspatialnetworkforminingdnamethylationdata AT changcheng groupshrinkagefeatureselectionwithaspatialnetworkforminingdnamethylationdata AT qianxiaohua groupshrinkagefeatureselectionwithaspatialnetworkforminingdnamethylationdata |