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Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data

Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data

Peter E. Larsen, Frank Collart, Yang Dai
Copyright: © 2010 |Volume: 1 |Issue: 3 |Pages: 19
ISSN: 1947-9115|EISSN: 1947-9123|EISBN13: 9781609609771|DOI: 10.4018/jkdb.2010070101
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MLA

Larsen, Peter E., et al. "Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data." IJKDB vol.1, no.3 2010: pp.1-19. http://doi.org/10.4018/jkdb.2010070101

APA

Larsen, P. E., Collart, F., & Dai, Y. (2010). Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 1(3), 1-19. http://doi.org/10.4018/jkdb.2010070101

Chicago

Larsen, Peter E., Frank Collart, and Yang Dai. "Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data," International Journal of Knowledge Discovery in Bioinformatics (IJKDB) 1, no.3: 1-19. http://doi.org/10.4018/jkdb.2010070101

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Abstract

The reconstruction of protein-protein interaction (PPI) networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These biological networks have specific topologies defined by the functional and evolutionary relationships between the proteins and the physical limitations imposed on proteins interacting in the three-dimensional space. In this paper, the authors propose a novel approach for the identification of potential protein-protein interactions based on the integration of known PPI network topology and transcriptomic data. The proposed method, Function Restricted Value Neighborhood (FRV-N), was used to reconstruct PPI networks using an experimental data set consisting of 170 yeast microarray profiles. The results of this analysis demonstrate that incorporating knowledge of interactome topology improves the ability of transcriptome analysis to reconstruct interaction networks with a high degree of biological relevance.

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