Identifying Overlapping Functional Modules in Protein Interaction Networks
Author Information
Author(s): Cho Young-Rae, Hwang Woochang, Ramanathan Murali, Zhang Aidong
Primary Institution: State University of New York, Buffalo, NY, USA
Hypothesis
Can integrating protein interaction networks with Gene Ontology annotations improve the identification of functional modules?
Conclusion
The integration of protein interaction networks with GO annotation data and the capability of detecting overlapping modules substantially improve the accuracy of module identification.
Supporting Evidence
- The algorithm showed higher accuracy compared to other competing approaches.
- Semantic similarity and semantic interactivity were positively correlated with functional co-occurrence.
- The study demonstrated that integrating GO annotations improved the reliability of protein interactions.
Takeaway
This study shows how combining different types of data can help scientists find groups of proteins that work together better.
Methodology
The study developed metrics for reliability of protein interactions and used a flow-based algorithm to identify overlapping modules in weighted interaction networks.
Limitations
The study may be limited by the quality of the input data and the assumptions made in the modularization algorithm.
Digital Object Identifier (DOI)
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