Semantic and layered protein function prediction from PPI networks

Zhu, Wei, Hou, Jingyu and Chen, Yi-Ping Phoebe 2010, Semantic and layered protein function prediction from PPI networks, Journal of theoretical biology, vol. 267, no. 2, pp. 129-136.

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Title Semantic and layered protein function prediction from PPI networks
Author(s) Zhu, Wei
Hou, Jingyu
Chen, Yi-Ping Phoebe
Journal name Journal of theoretical biology
Volume number 267
Issue number 2
Start page 129
End page 136
Total pages 8
Publisher Academic Press
Place of publication London, England
Publication date 2010
ISSN 0022-5193
1095-8541
Keyword(s) function prediction
protein-protein interaction
semantics
layered prediction
clustering
Summary Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010, Elsevier Ltd.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30033762

Document type: Journal Article
Collection: School of Information Technology
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