Huang, De-Shuang

Ji, Zhen

Lei, Ying-Ke

You, Zhu-Hong

Zhu, Lin

BMC Bioinformatics

English

PubMed

Abstract
          
            Background
            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.
          
          
            Results
            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.
          
          
            Conclusions
            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.
          </jats:sec

Ying-Ke Lei

Zhu-Hong You

Zhen Ji

Lin Zhu

De-Shuang Huang

Crossref

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

Springer - Publisher Connector

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3348017

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

Abstract

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Crossref

Springer - Publisher Connector

Springer - Publisher Connector