Evolutionary computation for disease gene association

Disease-gene association attempts to understand the relationship between genetic diseases and the genes associated with them. Many genetic diseases are not due to defects in a single gene, but rather are a result of various genetic components interacting in a complex network. We examine the use of two evolutionary computation approaches for disease-gene association, both using notions related to complex networks. When applied to the problem of identifying genes involved in Parkinson's Disease, both approaches compare favourably to other well-known frameworks. They also identify a number of genes that are strong candidates for further study for involvement in this disease.