Paper Info
Title
Inference of gene regulatory networks based on a universal minimum description length.
Abstract
The Boolean network paradigm is a simple and effective way to interpret genomic systems, but discovering the structure of these networks remains a difficult task. The minimum description length (MDL) principle has already been used for inferring genetic regulatory networks from time-series expression data and has proven useful for recovering the directed connections in Boolean networks. However, the existing method uses an ad hoc measure of description length that necessitates a tuning parameter for artificially balancing the model and error costs and, as a result, directly conflicts with the MDL principle's implied universality. In order to surpass this difficulty, we propose a novel MDL-based method in which the description length is a theoretical measure derived from a universal normalized maximum likelihood model. The search space is reduced by applying an implementable analogue of Kolmogorov's structure function. The performance of the proposed method is demonstrated on random synthetic networks, for which it is shown to improve upon previously published network inference algorithms with respect to both speed and accuracy. Finally, it is applied to time-series Drosophila gene expression measurements.
Year
DOI
Venue
2008
10.1155/2008/482090
EURASIP J. Bioinformatics and Systems Biology
Keywords
DocType
Volume
universal minimum description length,boolean network paradigm,description length,mdl principle,minimum description length,network inference algorithm,genetic regulatory network,novel mdl-based method,gene regulatory network,existing method,boolean network
Journal
2008,
Issue
ISSN
Citations 
1
1687-4145
19
PageRank 
References 
Authors
0.86
14
3
Name
Order
Citations
PageRank
John Dougherty1190.86
Ioan Tabus227638.23
Jaakko Astola31515230.41