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Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine

Author(s):

Ji-Yong An*, Yong Zhou, Lei Zhang, Qiang Niu and Da-Fu Wang   Pages 1 - 8 ( 8 )

Abstract:


Background: Self interacting proteins (SIPs) play an essential role in various aspects of the structural and functional organization of the cell. Objective: In the study, we presented a novelty sequence-based computational approach for predicting Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an Autocorrelation (AC) descriptor protein feature representation. Method: The major advantage of the proposed method mainly lies in adopting an effective feature extraction method to represent candidate self-interacting proteins by using the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and effective WELM classifier to perform classify. Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare our approach with the SVM classifier and existing sequence-based method on yeast and human dataset. Experimental results show that the performance of our method is better than several other state-of-the-art methods. Conclusion: It is demonstrated that the proposed method is suitable for SIPs detection and can execute incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor (PHP) for predicting SIPs. The web server including source code and the datasets are available at http://219.219.62.123:8888/WELMAC/.

Keywords:

SIPs, Weighed-Extreme Learning Machine, PSSM, Autocorrelation (AC) descriptor, PCA, Protein Sequence

Affiliation:

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116



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