Predictive Minimum Description Length Principle Approach to Inferring Gene Regulatory Networks
Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm is evaluated using both synthetic time series data sets and a biological time series data set (Saccharomyces cerevisiae). The results show that the proposed algorithm produced fewer false edges and significantly improved the precision when compared to existing MDL algorithm.
KeywordsMutual Information Gene Regulatory Network Boolean Network Minimum Description Length Conditional Mutual Information
- 3.John D, Tabus I, Astola J (2008) Inference of gene regulatory networks based on a universal minimum description length. EURASIP Journal on Bioinformatics and Systems Biology (published online April 15, 2008).Google Scholar
- 6.Liang S (1998) Reveal, A general reverse engineering algorithm for inference of genetic network architectures. Pacific Symposium on Biocomputing, 3, 18–29.Google Scholar
- 8.Grünwald PD, Myung IJ, Pitt MA (2005) Advances in minimum description length (Theory and Applications). The MIT Press, Cambridge, MA.Google Scholar
- 10.Chaitankar V, Zhang C, Ghosh P, Perkins EJ, Gong P, Deng Y (2009) Gene regulatory network inference using predictive minimum description length principle and conditional mutual information. Proceedings of International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 487–490.Google Scholar
- 11.Rissanen J (2006) An introduction to the MDL principle. Helsinki Institute for Information Technology, Tampere and Helsinki Universities of Technology, Finland, and University of London, England. (www.mdl-research.org/jorma.rissanen/pub/Intro.pdf).