Dataset Information

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 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.

SUBMITTER: Zhang P

PROVIDER: S-EPMC10865627 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.

Zhang Ping P Zhang Weihan W Sun Weicheng W Xu Jinsheng J Hu Hua H Wang Lei L Wong Leon L

BMC genomics 20240214 1

<h4>Background</h4>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.<h4>Results</h4>In this study, we present a multi-network representation learning framework called M-GBBD for the identifi ...[more]

PMID: 38350848

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Dataset Information

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.

Background

Results

Conclusion

Publications

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.

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