Graph kernel-based learning for gene function prediction from gene interaction network

Xin Li, Zhu Zhang, Hsinchun Chen, Jiexun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
Pages368-373
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007 - Fremont, CA, United States
Duration: Nov 2 2007Nov 4 2007

Publication series

NameProceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

Other

Other2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
CountryUnited States
CityFremont, CA
Period11/2/0711/4/07

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Graph kernel-based learning for gene function prediction from gene interaction network'. Together they form a unique fingerprint.

Cite this