ArticleTheoretical biology & medical modelling2011
Modularity analysis based on predicted protein-protein interactions provides new insights into pathogenicity and cellular process of Escherichia coli O157:H7.
Article in Theoretical biology & medical modelling, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Optimizing computational prediction of shrimp-WSSV protein interactions through integration of homology- and topology-based predictions.Molecular genetics and genomics : MGG · 2026Article
- HOXA1 is a radioresistance marker in multiple cancer types.Frontiers in oncology · 2022Article
- Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis.Bioengineered · 2021Article
- Analysis of Circular RNA-Related Competing Endogenous RNA Identifies the Immune-Related Risk Signature for Colorectal Cancer.Frontiers in genetics · 2020Article
- Identification of breast cancer-related circRNAs by analysis of microarray and RNA-sequencing data: An observational study.Medicine · 2019Observational
- Circular RNA-Associated Competing Endogenous RNA Network and Prognostic Nomogram for Patients With Colorectal Cancer.Frontiers in oncology · 2019Article
- Protein complex discovery by interaction filtering from protein interaction networks using mutual rank coexpression and sequence similarity.BioMed research international · 2015Article
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Authors and funding
7 authors.
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Abstract
backgroundWith the development of experimental techniques and bioinformatics, the quantity of data available from protein-protein interactions (PPIs) is increasing exponentially. Functional modules can be identified from protein interaction networks. It follows that the investigation of functional modules will generate a better understanding of cellular organization, processes, and functions. However, experimental PPI data are still limited, and no modularity analysis of PPIs in pathogens has been published to date.
resultsIn this study, we predict and analyze the functional modules of E. coli O157:H7 systemically by integrating several bioinformatics methods. After evaluation, most of the predicted modules are found to be biologically significant and functionally homogeneous. Six pathogenicity-related modules were discovered and analyzed, including novel modules. These modules provided new information on the pathogenicity of O157:H7. The modularity of cellular function and cooperativity between modules are also discussed. Moreover, modularity analysis of O157:H7 can provide possible candidates for biological pathway extension and clues for discovering new pathways of cross-talk.
conclusionsThis article provides the first modularity analysis of a pathogen and sheds new light on the study of pathogens and cellular processes. Our study also provides a strategy for applying modularity analysis to any sequenced organism.
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