INTRODUCTION

Computational approaches such as those described in Chapters 6 through 10 analyze protein-protein interaction (PPI) networks on the basis of network properties only, with little integration of information from outside sources. Current conventional methods can predict only whether two proteins share a specific function but not the universe of functions that they share. Their effectiveness is hampered by their inability to take into consideration the full range of available information about protein functions. The discussion in Chapter 11 has demonstrated the effectiveness of integrating Gene Ontology (GO) annotations into such analysis. It has become increasingly apparent that the fusion of multiple strands of biological data regarding each gene or protein will produce a more comprehensive picture of the relations among the components of a genome [191], including proteins, and a more specific representation of each protein. The sophisticated data set, graph, or tree generated through these means can be subjected to advanced computational analysis by methods such as machine learning algorithms. Such approaches have become increasingly widespread and are expected to improve the accuracy of protein function prediction.

In this chapter, we present some of the more recent approaches that have been developed for incorporating diverse biological information into the explorative analysis of PPI networks.

INTEGRATION OF GENE EXPRESSION WITH PPI NETWORKS

Current research efforts have resulted in the generation of large quantities of data related to the functional properties of genomes; specifically, gene expression and protein interaction data. Gene expression profiles provide a snapshot of the simultaneous activity of all the genes in a genome under a given condition, thus eliminating the need to examine each gene separately.