A study of gene prioritization algorithms on PPI networks

The undiscovered genes that contribute to a particular disease are of great interest in medical informatics. The candidate pool of such genes from GWAS or similar studies is typically vast. A direct experimental verification all such genes is prohibitively expensive in terms of time and money. In order to limit the number of candidates for experimental verification, numerous computational methods have been developed. One of the most widely used technique applies gene prioritization(GP) algorithms on a weighted protein-protein interaction network (PPIN). We analyzed the popular GP algorithms in semiautomatic manner. The idea is to study their behavior, rank them and attempt to improve their performance by modifying them. Additionally, we derived an expression for the steady state scores of the power method.