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Báo cáo hóa học: " Research Article Inference of Gene Regulatory Networks Based on a Universal Minimum Description Length"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Inference of Gene Regulatory Networks Based on a Universal Minimum Description Length | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2008 Article ID 482090 11 pages doi 10.1155 2008 482090 Research Article Inference of Gene Regulatory Networks Based on a Universal Minimum Description Length John Dougherty Ioan Tabus and Jaakko Astola Institute of Signal Processing Tampere University of Technology P.O. Box 553 33101 Tampere Finland Correspondence should be addressed to John Dougherty john.dougherty@tut.fi Received 24 August 2007 Accepted 11 January 2008 Recommended by Aniruddha Datta 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. Copyright 2008 John Dougherty et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is .