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BioMed Research International
Volume 2014 (2014), Article ID 598129, 9 pages
Research Article

Large-Scale Protein-Protein Interactions Detection by Integrating Big Biosensing Data with Computational Model

1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
2Department of Computing, Hong Kong Polytechnic University, Hong Kong
3Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Suzhou, Jiangsu 215163, China

Received 23 June 2014; Accepted 24 July 2014; Published 18 August 2014

Academic Editor: Jiangning Song

Copyright © 2014 Zhu-Hong You et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.