Laboratory for Computational Biology and Bioinformatics, Ecole Polytechnique Fédérale de Lausanne, Swiss Institute of Bioinformatics, EPFL IC IIF LCBB INJ211, Lausanne CH-1015, Switzerland.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Jul-Aug;9(4):1032-45. doi: 10.1109/TCBB.2012.62.
The experimental determination of transcriptional regulatory networks in the laboratory remains difficult and timeconsuming, 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.
在实验室中，实验确定转录调控网络仍然具有难度和耗时的特点，而推断这些网络的计算方法的准确性也只是中等水平。这种情况部分归因于单一生物体方法的局限性。计算生物学长期以来一直使用比较和进化方法来扩展其分析的范围和准确性。在本文中，我们描述了 ProPhyC，这是一种概率系统发育模型和相关推断算法，旨在通过利用这些生物体之间已知的进化关系，来提高对一组生物体的调控网络的推断。ProPhyC 可以与各种网络进化模型和任何现有的推断方法一起使用。在生物和合成数据上的广泛实验结果证实，我们的模型（通过其相关的细化算法）在推断网络的质量方面优于所有当前的方法。我们还将 ProPhyC 与我们设计的迁移学习方法进行了比较。该方法也在推断一组生物体的调控网络时使用了系统发育关系。使用类似的输入信息，但在非常不同的框架中设计，这种迁移学习方法的表现并不优于 ProPhyC，这表明 ProPhyC 很好地利用了进化信息。
