Improving inference of transcriptional regulatory networks based on network evolutionary models

Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present evolutionary approaches to improve the inference of regulatory networks for a family of organisms by developing an evolutionary model for these networks and taking advantage of established phylogenetic relationships among these organisms. In previous work, we used a simple evolutionary model for regulatory networks and provided extensive simulation results showing that phylogenetic information, combined with such a model, could be used to gain significant improvements on the performance of current inference algorithms.


Published in:
Proc. 9th Workshop on Algs. in Bioinformatics WABI'09, 412-425
Presented at:
9th Workshop on Algs. in Bioinformatics WABI'09
Year:
2009
Publisher:
Berlin, Springer
Keywords:
Laboratories:




 Record created 2009-10-14, last modified 2018-03-17


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