Computing gene-disease associations efficiently

Most common diseases are complex genetic traits, with genetic contributing to susceptibility to the diseases. Knowing the genes and their variations that involved in a disease is crucial for early intervention and the identification of techniques that can cure the disease. Experimental methods for determining gene-disease associations are laborious and time consuming. This created the need for computational methods to predict the candidate genes associated with diseases, which will be verified using experimental methods. However, most current computational methods may return a large spectrum of candidate genes, which makes their verification by the experimental methods to be time consuming and laborious. We propose in this paper a state-ofthe-art biological system called GDL that can overcome the above-mentioned limitation. It does so by short-listing the likely candidate genes involved in a disease to a small and tightly defined group that elicits the disease when work in concert. Since the number of predicted genes is small, the verification of their involvement in the disease by experimental methods will be highly efficient. GDL will help biologists focus their investigation on a small and tightly defined group of genes.