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Updated: Feb 26, 2026
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A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
Published on: July 18, 2013
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Benchmarking selected computational gene network growing tools in context of virus-host interactions
Biruhalem Taye1,2,3, Candida Vaz4, Vivek Tanavde4,5
1Bioinformatics Institute, A*STAR, 30 Biopolis Street #07-01 Matrix, Singapore, 138671, Singapore. biruhalem@bii.a-star.edu.sg.
Scientific Reports
|July 21, 2017
Summary
Network growing tools like STRING can identify influenza A virus host factors more effectively than random gene selection. Computational network extension significantly improves biological process detection, matching or exceeding experimental screening efficacy.
Area of Science:
- Bioinformatics
- Systems Biology
- Virology
Background:
- Network growing tools suggest gene/protein connections to build functional networks.
- Influenza A virus host interactions are a well-studied system for analysis.
Purpose of the Study:
- Compare network growing functions of GeneMANIA, STRING, and IPA.
- Evaluate their performance in identifying known influenza A virus host factors.
Main Methods:
- Utilized influenza host interactions as a model system.
- Compared three network growing tools (GeneMANIA, STRING, IPA).
- Assessed recovery of known host factors from siRNA screens using small and medium input gene sets.
Main Results:
- All three tools detected significantly more known host factors than random genes.
- STRING performed strongest overall for network growing.
- Network extension improved detection of Gene Ontology (GO) biological processes for host factors.
Conclusions:
- Computational network growing is effective for identifying host-pathogen interactions.
- Network extension significantly enhances the discovery of biological processes.
- This approach offers an alternative or complement to experimental screening methods for host factor identification.
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