ArticleBMC bioinformatics2016
PROPER: global protein interaction network alignment through percolation matching.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Exact p-values for global network alignments via combinatorial analysis of shared GO terms : REFANGO: Rigorous Evaluation of Functional Alignments of Networks using Gene Ontology.Journal of mathematical biology · 2024Article
- BioAlign: An Accurate Global PPI Network Alignment Algorithm.Evolutionary bioinformatics online · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Pairwise Biological Network Alignment Based on Discrete Bat Algorithm.Computational and mathematical methods in medicine · 2021Article
- SAlign-a structure aware method for global PPI network alignment.BMC bioinformatics · 2020Article
- LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules.BMC genomics · 2019Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe alignment of protein-protein interaction (PPI) networks enables us to uncover the relationships between different species, which leads to a deeper understanding of biological systems. Network alignment can be used to transfer biological knowledge between species. Although different PPI-network alignment algorithms were introduced during the last decade, developing an accurate and scalable algorithm that can find alignments with high biological and structural similarities among PPI networks is still challenging.
resultsIn this paper, we introduce a new global network alignment algorithm for PPI networks called PROPER. Compared to other global network alignment methods, our algorithm shows higher accuracy and speed over real PPI datasets and synthetic networks. We show that the PROPER algorithm can detect large portions of conserved biological pathways between species. Also, using a simple parsimonious evolutionary model, we explain why PROPER performs well based on several different comparison criteria.
conclusionsWe highlight that PROPER has high potential in further applications such as detecting biological pathways, finding protein complexes and PPI prediction. The PROPER algorithm is available at http://proper.epfl.ch .
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