  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 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-assumption-based methods, such as the algorithm
to minimize inconsistent connected genes and diffusion kernels.
