Aligning Multiple PPI Networks with Representation Learning on Networks
Aligning Multiple PPI Networks with Representation Learning on Networks
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DOI：
10.1109/bibm.2018.8621084
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Bo Song;Jianliang Gao;Hongliang Du;Zheng Chen;Xiaohua Hu
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文献类型：
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作者：
Bo Song;Jianliang Gao;Hongliang Du;Zheng Chen;Xiaohua Hu
蛋白质-蛋白质相互作用（PPI）网络比对是理解生物学基本知识的一个重要研究方向，如不同物种中保守的进化途径和功能保守的蛋白质等。现有的PPI网络比对方法试图通过比对来自不同物种的所有蛋白质来提高覆盖率。然而，需要承认一个基本的生物学理由，即不是一个物种中的每一个蛋白质都可以也不应该在其他物种中找到同源蛋白质。在本文中，我们提出了一种新的多PPI网络比对方法，试图只比对那些具有最大相似性的蛋白质。为了在计算相似性时提供更全面的支持，我们在比对过程中将网络的结构特征与生物特征结合在一起。对于结构特征，我们在PPI网络上应用了一种表示学习方法，该方法创建了一个低维向量嵌入网络中每个蛋白质的周围拓扑。该方法量化了网络的结构特征，并将其转化为向量相似度的计算，为确定网络拓扑相似度提供了一种新的方法。我们还提出了一种新的拓扑评价指标，可以更好地评估跨不同网络的比对结果的拓扑质量。生物学和拓扑学评估表明，我们的方法是有前途的，优于以前的多个对齐方法。
Protein-protein interaction (PPI) network alignment has been motivating researches for the comprehension of the underlying crucial biological knowledge, such as conserved evolutionary pathways and functionally conserved proteins throughout different species. Existing PPI network alignment methods have tried to improve the coverage ratio by aligning all proteins from different species. However, there is a fundamental biological justification needed to be acknowledged, that not every protein in a species can, nor should, find homologous proteins in other species. In this paper, we propose a novel approach for multiple PPI network alignment that tries to align only those proteins with the most similarities. To provide more comprehensive supports in computing the similarity, we integrate structural features of the networks together with biological characteristics during the alignment. For the structural features, we apply on PPI networks a representation learning method, which creates a low-dimensional vector embedding with the surrounding topologies of each protein in the network. This approach quantifies the structural features, and provides a new way to determine the topological similarity of the networks by transferring which as calculations in vector similarities. We also propose a new metric for the topological evaluation which can better assess the topological quality of the alignment results across different networks. Both biological and topological evaluations demonstrate our approach is promising and preferable against previous multiple alignment methods.