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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Protein2Vec: Aligning Multiple PPI Networks with Representation Learning
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Protein2Vec: Aligning Multiple PPI Networks with Representation Learning

机译：Protein2VEC：对齐多个PPI网络，具有表示学习

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摘要

Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different species. Existing methods of PPI network alignment often try to improve the coverage ratio of the alignment result by aligning all proteins from different species. However, there is a fundamental biological premise that needs to be considered carefully: not every protein in a species can, nor should, find its homologous proteins in other species. In this work, we propose a novel alignment method to map only those proteins with the most similarity throughout the PPI networks of multiple species. For the similarity features of the protein in the networks, we integrate both topological features with biological characteristics to provide enhanced supports for the alignment procedures. For topological features, we apply a representation learning method on the networks and generate a low dimensional vector embedding with its surrounding structural features for each protein. The topological similarity of proteins from different PPI networks can thus be transferred as the similarity of their corresponding vector representations, which provides a new way to comprehensively quantify the topological similarities between proteins. We also propose a new measure for the topological evaluation of the alignment results which better uncover the structural quality of the alignment across multiple networks. Both biological and topological evaluations on the alignment results of real datasets demonstrate our approach is promising and preferable against previous multiple alignment methods.
机译：蛋白质 - 蛋白质相互作用（PPI）网络对准在理解各种物种上具有功能同源蛋白质和保守的进化途径等至关重要的生物学知识来发挥重要作用。 PPI网络对准的现有方法通常尝试通过将来自不同物种的所有蛋白质对准来改善对准结果的覆盖率。然而，有一个基本的生物前提是需要仔细考虑的基本生物前提：不是物种中的每种蛋白质，也不应该在其他物种中找到其同源蛋白质。在这项工作中，我们提出了一种新的对准方法，以仅在多种物种的PPI网络中映射具有最相似性的蛋白质。对于网络中蛋白质的相似性特征，我们将两个拓扑特征与生物学特性集成，以提供增强的对准程序的支持。对于拓扑特征，我们在网络上应用一种表示学习方法，并产生具有其周围结构特征的低尺寸向量嵌入每个蛋白质。因此，来自不同PPI网络的蛋白质的拓扑相似性可以作为其相应的矢量表示的相似性转移，这提供了一种全面地量化蛋白质之间的拓扑相似性的新方法。我们还提出了一种新的措施，用于对准结果的拓扑评估，更好地揭示了多个网络的对齐结构质量。对真实数据集的对准结果的生物和拓扑评估展示了我们的方法是有前途的，并且优选针对先前的多种对准方法。

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