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

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4 Author(s)
Xin Li ; Dept. of Inf. Syst., City Univ. of Hong Kong, Kowloon Tong, China ; Hsinchun Chen ; Jiexun Li ; Zhu Zhang

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.

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Information Technology in Biomedicine, IEEE Transactions on  (Volume:14 ,  Issue: 1 )