ArticleBioinformatics (Oxford, England)2016
Local versus global biological network alignment.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Multilayer network alignment based on topological assessment via embeddings.BMC bioinformatics · 2023Article
- Supervised biological network alignment with graph neural networks.Bioinformatics (Oxford, England) · 2023Article
- Challenges and Limitations of Biological Network Analysis.Biotech (Basel (Switzerland)) · 2022Article
- An Extensive Assessment of Network Embedding in PPI Network Alignment.Entropy (Basel, Switzerland) · 2022Article
- BioAlign: An Accurate Global PPI Network Alignment Algorithm.Evolutionary bioinformatics online · 2022Article
- NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity.Bioinformatics (Oxford, England) · 2021Article
- Data-driven biological network alignment that uses topological, sequence, and functional information.BMC bioinformatics · 2021Article
- Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.Frontiers in genetics · 2021Review
- SAlign-a structure aware method for global PPI network alignment.BMC bioinformatics · 2020Article
- L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks.Scientific reports · 2020Article
- Pairwise Versus Multiple Global Network Alignment.IEEE access : practical innovations, open solutions · 2020Article
- Data-driven network alignment.PloS one · 2020Article
- Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.Frontiers in genetics · 2019Review
- From homogeneous to heterogeneous network alignment via colored graphlets.Scientific reports · 2018Article
- PrimAlign: PageRank-inspired Markovian alignment for large biological networks.Bioinformatics (Oxford, England) · 2018Article
- Alignment of dynamic networks.Bioinformatics (Oxford, England) · 2017Article
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3 authors.
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Abstract
motivationNetwork alignment (NA) aims to find regions of similarities between species' molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories.
resultsWe introduce new measures of alignment quality that allow for fair comparison of the different LNA and GNA outputs, as such measures do not exist. We provide user-friendly software for efficient alignment evaluation that implements the new and existing measures. We evaluate prominent LNA and GNA methods on synthetic and real-world biological networks. We study the effect on alignment quality of using different interaction types and confidence levels. We find that the superiority of one NA category over the other is context-dependent. Further, when we contrast LNA and GNA in the application of learning novel protein functional knowledge, the two produce very different predictions, indicating their complementarity. Our results and software provide guidelines for future NA method development and evaluation. AVAILABILITY AND IMPLEMENTATION: Software: http://www.nd.edu/~cone/LNA_GNA CONTACT: : tmilenko@nd.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.