A Stochastic Approach to Candidate Disease Gene Subnetwork Extraction

摘要

Experimental methods are beginning to define the networks of interacting genes and proteins that control most biological processes. There is significant interest in developing computational approaches to identify subnetworks that control specific processes or that may be involved in specific human diseases. Because genes associated with a particular disease (i.e., disease genes) are likely to be well connected within the interaction network, the challenge is to identify the most well-connected subnetworks from a large number of possible subnetworks. One way to do this is to search through chromosomal loci, each of which has many candidate disease genes, to find a subset of genes well connected in the interaction network. In order to identify a significantly connected subnetwork, however, an efficient method of selecting candidate genes from each locus needs to be addressed. In the current study, we describe a method to extract important candidate subnetworks from a set of loci, each containing numerous genes. The method is scalable with the size of the interaction networks. We have conducted simulations with our method and observed promising performance.