1. Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network
- Author
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Ping Zhang, Weihan Zhang, Weicheng Sun, Jinsheng Xu, Hua Hu, Lei Wang, and Leon Wong
- Subjects
Gene biomarkers ,Brain diseases ,Gene-disease associations prediction ,Multi-network topological semantics ,Graph convolutional network ,Biotechnology ,TP248.13-248.65 ,Genetics ,QH426-470 - Abstract
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.
- Published
- 2024
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