Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Wang, Derui and Hou, Jingyu 2015, Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions, Journal of bioinformatics and computational biology, vol. 13, no. 5, pp. 1-22, doi: 10.1142/S0219720015500262.

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Title Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions
Author(s) Wang, Derui
Hou, JingyuORCID iD for Hou, Jingyu orcid.org/0000-0002-6403-9786
Journal name Journal of bioinformatics and computational biology
Volume number 13
Issue number 5
Start page 1
End page 22
Total pages 22
Publisher World Scientific Publishing
Place of publication London, Eng.
Publication date 2015-10
ISSN 1757-6334
Keyword(s) PPI network
iterative model
protein function prediction
Summary Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.
Language eng
DOI 10.1142/S0219720015500262
Field of Research 080109 Pattern Recognition and Data Mining
080299 Computation Theory and Mathematics not elsewhere classified
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, World Scientific Publishing
Persistent URL http://hdl.handle.net/10536/DRO/DU:30079355

Document type: Journal Article
Collection: School of Information Technology
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