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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

22 Scopus Citations
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Author(s)

  • Xin Li
  • Hsinchun Chen
  • Jiexun Li
  • Zhu Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number5272443
Pages (from-to)119-128
Journal / PublicationIEEE Transactions on Information Technology in Biomedicine
Volume14
Issue number1
Online published29 Sept 2009
Publication statusPublished - Jan 2010

Link(s)

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.

Research Area(s)

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

Citation Format(s)

Gene function prediction with gene interaction networks: A context graph kernel approach. / Li, Xin; Chen, Hsinchun; Li, Jiexun et al.
In: IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 1, 5272443, 01.2010, p. 119-128.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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