Computational techniques for identifying networks of interrelated diseases

Abstract

Recently there has been a lot of interest in using computational techniques to build networks of protein-to-protein interactions, interacting gene networks and metabolic reactions. Many interesting and novel discoveries have been made using graph based structures using links and nodes to represent the relationships between proteins and genes. Analysis of graph networks has revealed that genes and proteins cooperate in modules performing specific functions and that there is crosstalk or overlap between modules. In this paper we take these ideas further and build upon current knowledge to build up a network of human related diseases based on graph theory and the concept of overlap or shared function. We explore the hypothesis that many human diseases are linked by common genetic modules, therefore a defect in one of any of the cooperating genes in a module may lead to a specific disease or related symptom. We build our networks using data and information extracted from several online databases along with supporting knowledge in the form of biological ontologies.