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Enhancing the functional content of eukaryotic protein interaction networks
- Source :
- PLoS ONE, Vol 9, Iss 10, p e109130 (2014), PLoS ONE
- Publication Year :
- 2014
- Publisher :
- Public Library of Science (PLoS), 2014.
-
Abstract
- Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.
- Subjects :
- Proteomics
Computer and Information Sciences
Similarity (geometry)
Biophysics
lcsh:Medicine
Biology
Machine learning
computer.software_genre
Bioinformatics
Biochemistry
Measure (mathematics)
Set (abstract data type)
Interaction network
Genetics
Data Mining
Biomacromolecule-Ligand Interactions
Protein Interactions
lcsh:Science
Structure (mathematical logic)
Graph rewriting
Multidisciplinary
business.industry
Physics
lcsh:R
Local area network
Eukaryota
Proteins
Biology and Life Sciences
Computational Biology
Genomics
Functional Genomics
Signaling Networks
Signal Processing
Physical Sciences
Engineering and Technology
lcsh:Q
Artificial intelligence
Information Technology
business
computer
Algorithms
Network Analysis
Protein Binding
Research Article
Network analysis
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 9
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....dd3767ba7288a15edd273e991d476728