ArticleJournal of computational biology : a journal of computational molecular cell biology2010
Detection of locally over-represented GO terms in protein-protein interaction networks.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 27 citations in OpenAlex.
- Implications of ZNF334 gene in lymph node metastasis of lung SCC: potential bypassing of cellular senescence.Journal of translational medicine · 2024Article
- PIGNON: a protein-protein interaction-guided functional enrichment analysis for quantitative proteomics.BMC bioinformatics · 2021Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
- Functional 5' UTR motif discovery with LESMoN: Local Enrichment of Sequence Motifs in biological Networks.Nucleic acids research · 2017Article
- Experimental and computational analysis of a large protein network that controls fat storage reveals the design principles of a signaling network.PLoS computational biology · 2015Article
- Scalable global alignment for multiple biological networks.BMC bioinformatics · 2012Article
- A network-based approach for predicting missing pathway interactions.PLoS computational biology · 2012Article
- Mapping the disease protein interactome: toward a molecular medicine GPS to accelerate drug and biomarker discovery.Journal of proteome research · 2011Article
- The power of protein interaction networks for associating genes with diseases.Bioinformatics (Oxford, England) · 2010Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
High-throughput methods for identifying protein-protein interactions produce increasingly complex and intricate interaction networks. These networks are extremely rich in information, but extracting biologically meaningful hypotheses from them and representing them in a human-readable manner is challenging. We propose a method to identify Gene Ontology terms that are locally over-represented in a subnetwork of a given biological network. Specifically, we propose several methods to evaluate the degree of clustering of proteins associated to a particular GO term in both weighted and unweighted PPI networks, and describe efficient methods to estimate the statistical significance of the observed clustering. We show, using Monte Carlo simulations, that our best approximation methods accurately estimate the true p-value, for random scale-free graphs as well as for actual yeast and human networks. When applied to these two biological networks, our approach recovers many known complexes and pathways, but also suggests potential functions for many subnetworks. Online Supplementary Material is available at www.liebertonline.com.
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