Functional Gene Networks and Their Applications

Abstract

One essential goal in functional genomics is to understand the functions and functional interactions of genes. The functional interaction between genes can happen in many ways and at different molecular levels, including co-expression, protein–protein interaction, shared sequence motif, etc. The functional interaction supported by such heterogeneous genomic data can be integrated into functional gene networks (FGNs) based on machine learning approaches. In FGNs, a node represents a gene and the edge indicates the probability that two genes co-function in the same pathway or biological process. By addressing the functional difference between isoforms generated from the same gene, recent efforts have focused on building FGNs at the finer isoform level, i.e., functional isoform networks (FINs). In this chapter, we will first present an introduction to FGNs and describe how heterogeneous genomic data can be integrated to build the network by machine learning approaches. We will then describe the refinement of FGNs from global networks to tissue-specific networks and from gene level to isoform level. Finally, we will describe and discuss the applications of FGNs in predicting gene functions and disease genes.