Gene function prediction with gene interaction networks: a context graph kernel approach.
Predicting gene functions is a challenge for biologists in the postgenomic 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.
Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong. xin.li@cityu.edu.hk
Xin
X
Li
Hsinchun
H
Chen
Jiexun
J
Li
Zhu
Z
Zhang
D000465:Algorithms; D001185:Artificial Intelligence; D053263:Gene Regulatory Networks; D005796:Genes / Q000502:physiology*; D016158:Genes, p53 / Q000502:physiology; D015894:Genome, Human; D006801:Humans; D049490:Systems Biology / Q000379:methods*