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

Xin Li, Hsinchun Chen, Jiexun Li, Zhu Zhang

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish (US)
Article number5272443
Pages (from-to)119-128
Number of pages10
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number1
StatePublished - Jan 2010


  • Classification
  • Gene pathway
  • Kernel-based method
  • System biology

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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