Modeling and characterization of disease associated subnetworks in the human interactome using machine learning.

Abstract

The availability of large-scale, genome-wide data about the molecular interactome of entire organisms has made possible new types of integrative studies, making use of rapidly accumulating knowledge of gene-disease associations. Previous studies have established the presence of functional biomodules in the molecular interaction network of living organisms, a number of which have been associated with the pathogenesis and progression of human disease. While a number of studies have examined the networks and biomodules associated with disease, the properties that contribute to the particular susceptibility of these subnetworks to disruptions leading to disease phenotypes have not been extensively studied. We take a machine learning approach to the characterization of these disease subnetworks associated with complex and single-gene diseases, taking into account both the biological roles of their constituent genes and topological properties of the networks they form.