Title
Aligning Multiple PPI Networks with Representation Learning on Networks
Abstract
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.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621084
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
protein representation,multiple nenvork alignment,PPI networks,topological assessment
Embedding,Computer science,Protein superfamily,Network alignment,Network topology,Artificial intelligence,Multiple sequence alignment,Feature learning,Machine learning
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5386-5489-7
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Bo Song174.15
Jianliang Gao210620.98
Hongliang Du300.34
Zheng Chen492.82
Xiaohua Hu52819314.15