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 net

SUBMITTER: Zhang P 

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

REPOSITORIES: biostudies-literature