Reconstructing optimal phylogenetic trees: a challenge in experimental algorithmics
Experimental algorithmics
Analyzing time series gene expression data
Bioinformatics
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
ProPhyC: a probabilistic phylogenetic model for refining regulatory networks
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Parsimonious reconstruction of network evolution
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
Refining Regulatory Networks through Phylogenetic Transfer of Information
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Inferring transcriptional regulatory networks from gene-expression data remains a challenging problem, in part because of the noisy nature of the data and the lack of strong network models. Time-series expression data have shown promise and recent work by Babu on the evolution of regulatory networks in E. coliand S. cerevisiaeopened another avenue of investigation. In this paper we take the evolutionary approach one step further, by developing ML-based refinement algorithms that take advantage of established phylogenetic relationships among a group of related organisms and of a simple evolutionary model for regulatory networks to improve the inference of these networks for these organisms from expression data gathered under similar conditions.We use simulations with different methods for generating gene-expression data, different phylogenies, and different evolutionary rates, and use different network inference algorithms, to study the performance of our algorithmic boosters. The results of simulations (including various tests to exclude confounding factors) demonstrate clear and significant improvements (in both specificity and sensitivity) on the performance of current inference algorithms. Thus gene-expression studies across a range of related organisms could yield significantly more accurate regulatory networks than single-organism studies.