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Published on: October 13, 2023
Mining disease genes using integrated protein-protein interaction and gene-gene co-regulation information
Jin Li1, Limei Wang2, Maozu Guo3
1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China ; School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China ; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China.
Abstract:
In humans, despite the rapid increase in disease-associated gene discovery, a large proportion of disease-associated genes are still unknown. Many network-based approaches have been used to prioritize disease genes. Many networks, such as the protein-protein interaction (PPI), KEGG, and gene co-expression networks, have been used. Expression quantitative trait loci (eQTLs) have been successfully applied for the determination of genes associated with several diseases. In this study, we constructed an eQTL-based gene-gene co-regulation network (GGCRN) and used it to mine for disease genes. We adopted the random walk with restart (RWR) algorithm to mine for genes associated with Alzheimer disease. Compared to the Human Protein Reference Database (HPRD) PPI network alone, the integrated HPRD PPI and GGCRN networks provided faster convergence and revealed new disease-related genes. Therefore, using the RWR algorithm for integrated PPI and GGCRN is an effective method for disease-associated gene mining.
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