Detangling PPI networks to uncover functionally meaningful clusters. - Hall-Swan, Sarah.; Crawford, Jake.; Newman, Rebecca.; Cowen, Lenore J. (2018)
Background: Decomposing a protein-protein interaction network (PPI network) into non-overlapping clusters or communities, sometimes called "network modules," is an important way to explore functional roles of sets of genes. When the method to accomplish this decomposition is solely based on purely graph-theoretic measures of the interconnection structure of the network, this is often called unsupervised clustering or community detection. In this study, we compare unsupervised computational methods for decomposing a PPI network into non-overlapping modules. A method is preferred if it results in a large proportion of nodes being assigned to functionally meaningful modules, as measured by functional enrichment over terms from the Gene Ontology (GO).; Keywords: PPI networks, Protein function prediction, Community detection, Diffusion state distance.; Springer Open.