Inferring gene regulatory networks from microarray time series data using transfer entropy

Thai Quang Tung*, Taewoo Ryu, Kwang H. Lee, Doheon Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

Reverse engineering of gene regulatory networks from microarray time series data has been a challenging problem due to the limit of available data. In this paper, a new approach is proposed based on the concept of transfer entropy. Using this information theoretic measure, causal relations between pairs of genes are assessed to draw a causal network. A heuristic rule is then applied to differentiate direct and indirect causality. Simulation on a synthetic network showed that the transfer entropy can identify both linear and nonlinear causality. Application of the method in a biological data identified many causal interactions with biological information supports.

Original languageEnglish (US)
Title of host publicationProceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07
Pages383-388
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07 - Maribor, Slovenia
Duration: Jun 20 2007Jun 22 2007

Other

Other20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07
CountrySlovenia
CityMaribor
Period06/20/0706/22/07

ASJC Scopus subject areas

  • Engineering(all)

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