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.
more »
« less
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.
more »
« less
- Award ID(s):
- 1650431
- NSF-PAR ID:
- 10087303
- Date Published:
- Journal Name:
- 2018 IEEE BIBM Conference Proceeding
- Page Range / eLocation ID:
- 136-141
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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