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This article is part of the supplement: Proceedings of the 2012 International Conference on Intelligent Computing (ICIC 2012)

Open Access Proceedings

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

Zhu-Hong You1*, Ying-Ke Lei2, Lin Zhu3, Junfeng Xia4 and Bing Wang5

Author Affiliations

1 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China

2 Department of Information, Electronic Engineering Institute, Hefei, Anhui 230601, China

3 Department of Automation, Un iversity of Science and Technology of China, Hefei, Anhui 230601, China

4 Institute of Health Sciences, Anhui University, Hefei, Anhui 230601, China

5 The Advanced Research Institute of Intelligent Sensing Network, Tongji University, shanghai, 201804, China

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BMC Bioinformatics 2013, 14(Suppl 8):S10  doi:10.1186/1471-2105-14-S8-S10

Published: 9 May 2013

Abstract

Background

Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs.

Results

We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance.

Conclusions

When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.