Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression

Abstract

The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but also to enhance human disease diagnosis and treatment. Recently, high-throughput techniques have been applied to detect dozens or even hundreds of candidate genes. However, experimental approaches to validate the many candidates are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Therefore, numerous theoretical and computational methods (e.g. network-based approaches) have been developed to prioritize candidate disease genes. Many network-based approaches implicitly utilize the observation that genes causing the same or similar diseases tend to correlate with each other in gene-protein relationship networks. Of these network approaches, the random walk with restart algorithm (RWR) is considered to be a state-of-the-art approach. To further improve the performance of RWR, we propose a novel method named ESFSC to identify disease-related genes, by enlarging the seed set according to the centrality of disease genes in a network and fusing information of the protein-protein interaction (PPI) network topological similarity and the gene expression correlation. The ESFSC algorithm restarts at all of the nodes in the seed set consisting of the known disease genes and their k-nearest neighbor nodes, then walks in the global network separately guided by the similarity transition matrix constructed with PPI network topological similarity properties and the correlational transition matrix constructed with the gene expression profiles. As a result, all the genes in the network are ranked by weighted fusing the above results of the RWR guided by two types of transition matrices. Comprehensive simulation results of the 10 diseases with 97 known disease genes collected from the Online Mendelian Inheritance in Man (OMIM) database show that ESFSC outperforms existing methods for prioritizing candidate disease genes. The top prediction results of Alzheimer's disease are consistent with previous literature reports.

m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

“…According to the probability that the walker would reach the node after the RWR enters the stable state, nodes ranked top 10 are held as candidate DmMGs. Previous studies suggested various ways to select candidate nodes including using the most accessible node (i.e., top 1) [58, 59], top 5 [60], top 10 [61,62], top 20 [62], and top 100 nodes [63], but no consensus rules have been proposed. In this work, we retained the top 10 accessible nodes to keep a balanced tradeoff between choosing too few informative genes (e.g.…”

Prioritisation and Network Analysis of Crohn's Disease Susceptibility Genes

“…All of these methods depend on the topological structure of the interactome; but, while linkage and neighbourhood based methods rely upon a particular topological metric, such as pairwise or nearest interactions, diffusion based methods adopt the full information of the network topology. Diffusion-based methods have been recently applied and shown to achieve the state-of-the-art predictive performance [12] , [13] , [14] , [15] ; in addition, combining predictions made by different methods in a 'consensus method' yielded to Pareto optimal performance in the precision-recall objectives [15] .…”

A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms

“…This set contained 123 unique genes. These databases which were source of the gene set 2 were used as the evaluation set in other studies for gene prioritization [18-20]. …”