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Protein2Vec: Aligning Multiple PPI Networks with Representation Learning
Journal article   Open access

Protein2Vec: Aligning Multiple PPI Networks with Representation Learning

Jianliang Gao, Ling Tian, Tengfei Lv, Jianxin Wang, Bo Song and Xiaohua Hu
IEEE/ACM transactions on computational biology and bioinformatics, v 18(1)
Jan 2021
DOI: https://doi.org/10.1109/TCBB.2019.2937771
PMID: 31478867
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
http://arxiv.org/abs/1201.1694View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Knowledge engineering Measurement multiple network alignment Network topology PPI networks Protein representation Proteins topological assessment Topology Vegetation
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.

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25 Record Views
7 citations in Web of Science
9 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Biochemical Research Methods
Computer Science, Interdisciplinary Applications
Mathematics, Interdisciplinary Applications
Statistics & Probability
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