Abstract

Most existing approaches for gene selection are based on evaluating statistical relevance. However, there are remarkable discrepancies between statistical relevance and biological relevance. It is important to consider biological relevance for crucial genes identification. The task of detecting biological relevance presents two major challenges: first, how to define different types of measures to evaluate the biological relevance from multiple perspectives; and second, how to effectively integrate these measures to achieve better estimations. In this work, we propose to detect biological relevance by applying dynamics analysis using both biological networks and gene expression profiles from different phenotypes. We also develop an effective probabilistic model to integrate various types of relevance measures in a unified form. Experimental results show the efficacy and potential of the proposed approach with promising findings.

Original languageEnglish (US)
Title of host publication2nd International Conference on Bioinformatics and Computational Biology 2010, BICoB 2010
Pages44-49
Number of pages6
StatePublished - Dec 1 2010
Event2nd International Conference on Bioinformatics and Computational Biology 2010, BICoB 2010 - Honolulu, HI, United States
Duration: Mar 24 2010Mar 26 2010

Publication series

Name2nd International Conference on Bioinformatics and Computational Biology 2010, BICoB 2010

Other

Other2nd International Conference on Bioinformatics and Computational Biology 2010, BICoB 2010
CountryUnited States
CityHonolulu, HI
Period3/24/103/26/10

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ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Information Management

Cite this

Zhao, Z., Liu, H., Wang, J., & Chang, Y. (2010). Biological relevance detection via network dynamic analysis. In 2nd International Conference on Bioinformatics and Computational Biology 2010, BICoB 2010 (pp. 44-49). (2nd International Conference on Bioinformatics and Computational Biology 2010, BICoB 2010).