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A Universal Minimum Description Length-Based Algorithm for Inferring the Structure of Genetic Networks

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3 Author(s)
John Dougherty ; Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, FI-33101 Tampere, Finland. ; Ioan Tabus ; Jaakko Astola

The Boolean network paradigm is a simple and effective way to interpret genomic systems, but discovering the structure of these networks is a difficult task. In this paper, we model genetic time series data as multivariate Boolean regression and employ the minimum description length principle to find significant relationships among the genes. The description length is based upon a universal normalized maximum likelihood model, and we use an analogue of Kolmogorov's structure function to reduce computation time. The performance of the proposed method is demonstrated on random synthetic networks.

Published in:

2007 IEEE International Workshop on Genomic Signal Processing and Statistics

Date of Conference:

10-12 June 2007