Gene function prediction with gene interaction networks: a context graph kernel approach

Authors:
Xin Li;Hsinchun Chen;Jiexun Li;Zhu Zhang
Affiliations:
Department of Information Systems, City University of Hong Kong, Kowloon Tong, Hong Kong;Department of Management Information Systems, University of Arizona, Tucson, AZ;College of Information Science and Technology, Drexel University, Philadelphia, PA;Department of Management Information Systems, University of Arizona, Tucson, AZ
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2010

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Abstract

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.