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This article is part of the supplement: Advanced intelligent computing theories and their applications in bioinformatics. Proceedings of the 2011 International Conference on Intelligent Computing (ICIC 2011)

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Assessing and predicting protein interactions by combining manifold embedding with multiple information integration

Ying-Ke Lei12, Zhu-Hong You13, Zhen Ji3, Lin Zhu4 and De-Shuang Huang1*

Author affiliations

1 Tongji University, 1239 Siping Road, Shanghai, P.R. China

2 Department of Information, Electronic Engineering Institute, Hefei, Anhui 230027, P.R. China

3 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, P.R. China

4 Department of Automation, University of Science and Technology of China, Hefei, Anhui 230027, P.R. China

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 7):S3  doi:10.1186/1471-2105-13-S7-S3

Published: 8 May 2012



Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. Over the last decade, the development of novel high-throughput techniques has resulted in enormous amounts of data and provided valuable resources for studying protein interactions. However, these high-throughput protein interaction data are often associated with high false positive and false negative rates. It is therefore highly desirable to develop scalable methods to identify these errors from the computational perspective.


We have developed a robust computational technique for assessing the reliability of interactions and predicting new interactions by combining manifold embedding with multiple information integration. Validation of the proposed method was performed with extensive experiments on densely-connected and sparse PPI networks of yeast respectively. Results demonstrate that the interactions ranked top by our method have high functional homogeneity and localization coherence.


Our proposed method achieves better performances than the existing methods no matter assessing or predicting protein interactions. Furthermore, our method is general enough to work over a variety of PPI networks irrespectively of densely-connected or sparse PPI network. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks.