Gene Regulatory Network Inference Using Predictive Minimum Description Length Principle and Conditional Mutual Information

Authors:
Vijender Chaitankar;Chaoyang Zhang;Preetam Ghosh;Edward J. Perkins;Ping Gong;Youping Deng
Affiliations:
-;-;-;-;-;-
Venue:
IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
Year:
2009

Citing 0
Cited 2

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Genome scale inference of transcriptional regulatory networks using mutual information on complex interactions

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Abstract

Inferring gene regulatory networks using information theory models have received much attention due to their simplicity and low computational costs. One of the major problems with information theory models is to determine the threshold which defines the regulatory relationships between genes. The minimum description length (MDL) has been used to overcome this problem. We propose an inference algorithm which incorporates mutual information (MI), conditional mutual information (CMI) and predictive minimum description length (PMDL) principles to infer gene regulatory networks from microarray data. The information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle determines the MI threshold. The performance of the proposed algorithm is demonstrated on random synthetic networks, and the results show that the PMDL principle is a good choice to determine the MI threshold.