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

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4 Author(s)
Thai Quang Tung ; Korea Adv. Institue of Sci. & Technol., Daejeon ; Taewoo Ryu ; Lee, K.H. ; Doheon Lee

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.

Published in:

Computer-Based Medical Systems, 2007. CBMS '07. Twentieth IEEE International Symposium on

Date of Conference:

20-22 June 2007