Identification of genes which cause a particular disease in humans is a main objective of hereditary genetics. Such genes are said to be disease genes. Several natural techniques are accessible to recognize disease genes. The candidate genes should be additionally explored to distinguish the disease causing genes. Scholars must organize the genes from most to minimum promising while carrying out the validation procedure. This is essential for reducing the cost required for experimental testing. The idea here is to prioritize candidate genes in a protein-protein interaction network based on community detection. We implement a method to prioritize the candidate genes in PPI network. We tested the efficiency of our algorithm over the existing algorithms.
Indulekha T.S., S, A. G., and Sudhakaran, P., “A Graph Based Algorithm for Clustering and Ranking Proteins for Identifying Disease Causing Genes”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018.