A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on inductive transfer
A tutorial on learning with Bayesian networks
Learning in graphical models
Reconstructing optimal phylogenetic trees: a challenge in experimental algorithmics
Experimental algorithmics
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Improving inference of transcriptional regulatory networks based on network evolutionary models
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
ProPhyC: a probabilistic phylogenetic model for refining regulatory networks
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
A hybrid micro-macroevolutionary approach to gene tree reconstruction
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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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.