ArticleCancer informatics2010
Optimal network alignment with graphlet degree vectors.
Article in Cancer informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- GTAT: empowering graph neural networks with cross attention.Scientific reports · 2025Article
- 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
- Multilayer network alignment based on topological assessment via embeddings.BMC bioinformatics · 2023Article
- Supervised biological network alignment with graph neural networks.Bioinformatics (Oxford, England) · 2023Article
- SANA: cross-species prediction of Gene Ontology GO annotations via topological network alignment.NPJ systems biology and applications · 2022Article
- 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
- The protein-protein interaction network alignment using recurrent neural network.Medical & biological engineering & computing · 2021Article
- Juxtapose: a gene-embedding approach for comparing co-expression networks.BMC bioinformatics · 2021Article
- Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.Frontiers in genetics · 2021Review
- L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks.Scientific reports · 2020Article
- Kernel Differential Subgraph Analysis to Reveal the Key Period Affecting Glioblastoma.Biomolecules · 2020Article
- Pairwise Versus Multiple Global Network Alignment.IEEE access : practical innovations, open solutions · 2020Article
- A review of protein-protein interaction network alignment: From pathway comparison to global alignment.Computational and structural biotechnology journal · 2020Review
- NAIGO: An Improved Method to Align PPI Networks Based on Gene Ontology and Graphlets.Frontiers in bioengineering and biotechnology · 2020Article
- NAPAbench 2: A network synthesis algorithm for generating realistic protein-protein interaction (PPI) network families.PloS one · 2020Article
- A Guide to Conquer the Biological Network Era Using Graph Theory.Frontiers in bioengineering and biotechnology · 2020Review
- Comparing methods for comparing networks.Scientific reports · 2019Article
- Functional protein representations from biological networks enable diverse cross-species inference.Nucleic acids research · 2019Article
- From homogeneous to heterogeneous network alignment via colored graphlets.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.
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Registered trials
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.