Inferring Gene Regulatory Networks from Microarray Time Series Data Using Transfer Entropy

Authors:
Thai Quang Tung;Taewoo Ryu;Kwang H. Lee;Doheon Lee
Affiliations:
Korea Advanced Institute of Science and Technology, Korea;Korea Advanced Institute of Science and Technology, Korea;Korea Advanced Institute of Science and Technology, Korea;Korea Advanced Institute of Science and Technology, Korea
Venue:
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Year:
2007

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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.