Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning
Cerebral ischemic stroke (IS) is a complex disease caused by multiple factors including vascular risk factors, genetic factors, and environment factors, which accentuates the difficulty in discovering corresponding disease-related genes. Identifying the genes associated with IS is critical for under...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.728333/full |
| _version_ | 1818606778692141056 |
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| author | Haijie Liu Liping Hou Shanhu Xu He Li Xiuju Chen Juan Gao Ziwen Wang Bo Han Xiaoli Liu Shu Wan |
| author_facet | Haijie Liu Liping Hou Shanhu Xu He Li Xiuju Chen Juan Gao Ziwen Wang Bo Han Xiaoli Liu Shu Wan |
| author_sort | Haijie Liu |
| collection | DOAJ |
| description | Cerebral ischemic stroke (IS) is a complex disease caused by multiple factors including vascular risk factors, genetic factors, and environment factors, which accentuates the difficulty in discovering corresponding disease-related genes. Identifying the genes associated with IS is critical for understanding the biological mechanism of IS, which would be significantly beneficial to the diagnosis and clinical treatment of cerebral IS. However, existing methods to predict IS-related genes are mainly based on the hypothesis of guilt-by-association (GBA). These methods cannot capture the global structure information of the whole protein–protein interaction (PPI) network. Inspired by the success of network representation learning (NRL) in the field of network analysis, we apply NRL to the discovery of disease-related genes and launch the framework to identify the disease-related genes of cerebral IS. The utilized framework contains three main parts: capturing the topological information of the PPI network with NRL, denoising the gene feature with the participation of a stacked autoencoder (SAE), and optimizing a support vector machine (SVM) classifier to identify IS-related genes. Superior to the existing methods on IS-related gene prediction, our framework presents more accurate results. The case study also shows that the proposed method can identify IS-related genes. |
| first_indexed | 2024-12-16T14:16:16Z |
| format | Article |
| id | doaj.art-f1159f795d424dcc82acefb24ede06c7 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-16T14:16:16Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-f1159f795d424dcc82acefb24ede06c72022-12-21T22:28:36ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-09-011210.3389/fgene.2021.728333728333Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation LearningHaijie Liu0Liping Hou1Shanhu Xu2He Li3Xiuju Chen4Juan Gao5Ziwen Wang6Bo Han7Xiaoli Liu8Shu Wan9Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, ChinaDepartment of Clinical Laboratory, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, ChinaAffiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Automation, College of Information Science and Engineering, Tianjin Tianshi College, Tianjin, ChinaDepartment of Neurology, Tianjin Nankai Hospital, Tianjin, ChinaDepartment of Neurology, Baoding No. 1 Central Hospital, Baoding, ChinaGraduate School of Chengde Medical College, Chengde, ChinaDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, ChinaAffiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaAffiliated Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaCerebral ischemic stroke (IS) is a complex disease caused by multiple factors including vascular risk factors, genetic factors, and environment factors, which accentuates the difficulty in discovering corresponding disease-related genes. Identifying the genes associated with IS is critical for understanding the biological mechanism of IS, which would be significantly beneficial to the diagnosis and clinical treatment of cerebral IS. However, existing methods to predict IS-related genes are mainly based on the hypothesis of guilt-by-association (GBA). These methods cannot capture the global structure information of the whole protein–protein interaction (PPI) network. Inspired by the success of network representation learning (NRL) in the field of network analysis, we apply NRL to the discovery of disease-related genes and launch the framework to identify the disease-related genes of cerebral IS. The utilized framework contains three main parts: capturing the topological information of the PPI network with NRL, denoising the gene feature with the participation of a stacked autoencoder (SAE), and optimizing a support vector machine (SVM) classifier to identify IS-related genes. Superior to the existing methods on IS-related gene prediction, our framework presents more accurate results. The case study also shows that the proposed method can identify IS-related genes.https://www.frontiersin.org/articles/10.3389/fgene.2021.728333/fullcerebral ischemic strokenetwork embeddingdisease gene predictionPPI networknetwork representation learning |
| spellingShingle | Haijie Liu Liping Hou Shanhu Xu He Li Xiuju Chen Juan Gao Ziwen Wang Bo Han Xiaoli Liu Shu Wan Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning Frontiers in Genetics cerebral ischemic stroke network embedding disease gene prediction PPI network network representation learning |
| title | Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning |
| title_full | Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning |
| title_fullStr | Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning |
| title_full_unstemmed | Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning |
| title_short | Discovering Cerebral Ischemic Stroke Associated Genes Based on Network Representation Learning |
| title_sort | discovering cerebral ischemic stroke associated genes based on network representation learning |
| topic | cerebral ischemic stroke network embedding disease gene prediction PPI network network representation learning |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.728333/full |
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