Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network
Abstract Background Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain network...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-024-09967-9 |
| _version_ | 1797275756242403328 |
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| author | Ping Zhang Weihan Zhang Weicheng Sun Jinsheng Xu Hua Hu Lei Wang Leon Wong |
| author_facet | Ping Zhang Weihan Zhang Weicheng Sun Jinsheng Xu Hua Hu Lei Wang Leon Wong |
| author_sort | Ping Zhang |
| collection | DOAJ |
| description | Abstract Background Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. Results In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback–Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. Conclusion Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning. |
| first_indexed | 2024-03-07T15:18:59Z |
| format | Article |
| id | doaj.art-68e0a850589d427cb19838bf74e0806e |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-03-07T15:18:59Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-68e0a850589d427cb19838bf74e0806e2024-03-05T17:46:51ZengBMCBMC Genomics1471-21642024-02-0125111610.1186/s12864-024-09967-9Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional networkPing Zhang0Weihan Zhang1Weicheng Sun2Jinsheng Xu3Hua Hu4Lei Wang5Leon Wong6College of Information Science and Engineering, Zaozhuang UniversityCAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Hubei Hongshan LaboratoryCollege of Informatics, Huazhong Agricultural UniversityCollege of Informatics, Huazhong Agricultural UniversityCollege of Information Science and Engineering, Zaozhuang UniversityCollege of Information Science and Engineering, Zaozhuang UniversityCollege of Big Data and Internet, Shenzhen Technology UniversityAbstract Background Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. Results In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback–Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. Conclusion Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.https://doi.org/10.1186/s12864-024-09967-9Gene biomarkersBrain diseasesGene-disease associations predictionMulti-network topological semanticsGraph convolutional network |
| spellingShingle | Ping Zhang Weihan Zhang Weicheng Sun Jinsheng Xu Hua Hu Lei Wang Leon Wong Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network BMC Genomics Gene biomarkers Brain diseases Gene-disease associations prediction Multi-network topological semantics Graph convolutional network |
| title | Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network |
| title_full | Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network |
| title_fullStr | Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network |
| title_full_unstemmed | Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network |
| title_short | Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network |
| title_sort | identification of gene biomarkers for brain diseases via multi network topological semantics extraction and graph convolutional network |
| topic | Gene biomarkers Brain diseases Gene-disease associations prediction Multi-network topological semantics Graph convolutional network |
| url | https://doi.org/10.1186/s12864-024-09967-9 |
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