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Issue No.04 - July-Aug. (2012 vol.9)
pp: 1032-1045
B. M. E. Moret , Lab. for Comput. Biol. & Bioinf., Ecole Polytech. Fed. de Lausanne, Lausanne, Switzerland
ABSTRACT
The experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-consuming, while computational methods to infer these networks provide only modest accuracy. The latter can be attributed partly to the limitations of a single-organism approach. Computational biology has long used comparative and evolutionary approaches to extend the reach and accuracy of its analyses. In this paper, we describe ProPhyC, a probabilistic phylogenetic model and associated inference algorithms, designed to improve the inference of regulatory networks for a family of organisms by using known evolutionary relationships among these organisms. ProPhyC can be used with various network evolutionary models and any existing inference method. Extensive experimental results on both biological and synthetic data confirm that our model (through its associated refinement algorithms) yields substantial improvement in the quality of inferred networks over all current methods. We also compare ProPhyC with a transfer learning approach we design. This approach also uses phylogenetic relationships while inferring regulatory networks for a family of organisms. Using similar input information but designed in a very different framework, this transfer learning approach does not perform better than ProPhyC, which indicates that ProPhyC makes good use of the evolutionary information.
INDEX TERMS
probability, bioinformatics, biological techniques, complex networks, evolution (biological), genetics, inference mechanisms, evolutionary information, regulatory network refinement, phylogenetic information transfer, transcriptional regulatory networks, single organism approach, computational biology, ProPhyC, probabilistic phylogenetic model, inference algorithms, regulatory network inference, evolutionary relationships, network evolutionary models, transfer learning approach, Computational modeling, Phylogeny, Biological system modeling, Inference algorithms, Vegetation, Algorithm design and analysis, Organisms, transfer learning., Regulatory networks, network inference, evolution, phylogenetic relationships, ancestral network, refinement, gene duplication, evolutionary model, evolutionary history, reconciliation, maximum likelihood
CITATION
B. M. E. Moret, "Refining Regulatory Networks through Phylogenetic Transfer of Information", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 4, pp. 1032-1045, July-Aug. 2012, doi:10.1109/TCBB.2012.62
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