Information theoretic methods for modeling of gene regulatory networks

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

7 Citations (Scopus)

Abstract

This paper reviews the information theoretic methods used for inferring gene regulatory networks. Mutual information has been widely used as a dependency measure to estimate the undirected interactions between genes using steady state data. However, employing time-series data results in a directed graph. Since two genes may be interacting with each other via an intermediate gene, their mutual information may show a direct dependency. To resolve this issue, data processing inequality and conditional mutual information have been employed. Mutual information, being a symmetric measure, is unable to predict directed edges using the steady-state data alone, while algorithms using time-series data can be computationally complex as more data is involved. Therefore, non-symmetric measures such as mixing coefficients have recently been proposed in the literature. The algorithms using these techniques are also discussed in this article. Estimation of information-theoretic metrics is explained which is a core component of all the methods. Performance metrics that are frequently used to test the robustness and accuracy of the algorithms are also described and some avenues of future research are proposed.

Original languageEnglish
Title of host publication2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
Pages418-423
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012 - San Diego, CA, United States
Duration: 9 May 201212 May 2012

Other

Other2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012
CountryUnited States
CitySan Diego, CA
Period9/5/1212/5/12

Fingerprint

Genes
Time series
Directed graphs

Keywords

  • Gene regulatory network
  • information theory
  • mutual information

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Noor, A., Serpedin, E., Nounou, M., Nounou, H., Mohamed, N., & Chouchane, L. (2012). Information theoretic methods for modeling of gene regulatory networks. In 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012 (pp. 418-423). [6217260] https://doi.org/10.1109/CIBCB.2012.6217260

Information theoretic methods for modeling of gene regulatory networks. / Noor, Amina; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem; Mohamed, Nady; Chouchane, Lotfi.

2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012. 2012. p. 418-423 6217260.

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

Noor, A, Serpedin, E, Nounou, M, Nounou, H, Mohamed, N & Chouchane, L 2012, Information theoretic methods for modeling of gene regulatory networks. in 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012., 6217260, pp. 418-423, 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012, San Diego, CA, United States, 9/5/12. https://doi.org/10.1109/CIBCB.2012.6217260
Noor A, Serpedin E, Nounou M, Nounou H, Mohamed N, Chouchane L. Information theoretic methods for modeling of gene regulatory networks. In 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012. 2012. p. 418-423. 6217260 https://doi.org/10.1109/CIBCB.2012.6217260
Noor, Amina ; Serpedin, Erchin ; Nounou, Mohamed ; Nounou, Hazem ; Mohamed, Nady ; Chouchane, Lotfi. / Information theoretic methods for modeling of gene regulatory networks. 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012. 2012. pp. 418-423
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