Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach

Predicting gene functions is a challenge for biologists in the post-genomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine learning framework, we design a context graph kernel (CGK) to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumptionbased methods, such as GAIN and diffusion kernels. Introduction Developments in genome sequencing have led to the identification of a large number of genes. However, most of these genes' functions remain poorly known or unknown [1].