ArticleBMC bioinformatics2008
Interrogating domain-domain interactions with parsimony based approaches.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions.PLoS computational biology · 2021Article
- Inference of domain-disease associations from domain-protein, protein-disease and disease-disease relationships.BMC systems biology · 2016Article
- Inferring domain-domain interactions from protein-protein interactions with formal concept analysis.PloS one · 2014Article
- Protein-protein interaction networks (PPI) and complex diseases.Gastroenterology and hepatology from bed to bench · 2014Review
- Reconstituting protein interaction networks using parameter-dependent domain-domain interactions.BMC bioinformatics · 2013Article
- IDDI: integrated domain-domain interaction and protein interaction analysis system.Proteome science · 2012Article
- Chapter 4: Protein interactions and disease.PLoS computational biology · 2012Article
- Inferring domain-domain interactions from protein-protein interactions in the complex network conformation.BMC systems biology · 2012Article
- DOMINE: a comprehensive collection of known and predicted domain-domain interactions.Nucleic acids research · 2011Article
- A top-down approach to infer and compare domain-domain interactions across eight model organisms.PloS one · 2009Article
- Algorithmic and analytical methods in network biology.Wiley interdisciplinary reviews. Systems biology and medicineReview
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2 authors.
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Abstract
backgroundThe identification and characterization of interacting domain pairs is an important step towards understanding protein interactions. In the last few years, several methods to predict domain interactions have been proposed. Understanding the power and the limitations of these methods is key to the development of improved approaches and better understanding of the nature of these interactions.
resultsBuilding on the previously published Parsimonious Explanation method (PE) to predict domain-domain interactions, we introduced a new Generalized Parsimonious Explanation (GPE) method, which (i) adjusts the granularity of the domain definition to the granularity of the input data set and (ii) permits domain interactions to have different costs. This allowed for preferential selection of the so-called "co-occurring domains" as possible mediators of interactions between proteins. The performance of both variants of the parsimony method are competitive to the performance of the top algorithms for this problem even though parsimony methods use less information than some of the other methods. We also examined possible enrichment of co-occurring domains and homo-domains among domain interactions mediating the interaction of proteins in the network. The corresponding study was performed by surveying domain interactions predicted by the GPE method as well as by using a combinatorial counting approach independent of any prediction method. Our findings indicate that, while there is a considerable propensity towards these special domain pairs among predicted domain interactions, this overrepresentation is significantly lower than in the iPfam dataset.
conclusionThe Generalized Parsimonious Explanation approach provides a new means to predict and study domain-domain interactions. We showed that, under the assumption that all protein interactions in the network are mediated by domain interactions, there exists a significant deviation of the properties of domain interactions mediating interactions in the network from that of iPfam data.
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