JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning MethodReport as inadecuate

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BioMed Research International - Volume 2015 2015, Article ID 705156, 12 pages -

Research Article

School of Control Science and Engineering, Shandong University, Jinan 250061, China

School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China

Received 3 August 2015; Revised 5 October 2015; Accepted 11 October 2015

Academic Editor: Tatsuya Akutsu

Copyright © 2015 Lina Zhang 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.


Different types of J-proteins perform distinct functions in chaperone processes and diseases development. Accurate identification of types of J-proteins will provide significant clues to reveal the mechanism of J-proteins and contribute to developing drugs for diseases. In this study, an ensemble predictor called JPPRED for J-protein prediction is proposed with hybrid features, including split amino acid composition SAAC, pseudo amino acid composition PseAAC, and position specific scoring matrix PSSM. To deal with the imbalanced benchmark dataset, the synthetic minority oversampling technique SMOTE and undersampling technique are applied. The average sensitivity of JPPRED based on above-mentioned individual feature spaces lies in the range of 0.744–0.851, indicating the discriminative power of these features. In addition, JPPRED yields the highest average sensitivity of 0.875 using the hybrid feature spaces of SAAC, PseAAC, and PSSM. Compared to individual base classifiers, JPPRED obtains more balanced and better performance for each type of J-proteins. To evaluate the prediction performance objectively, JPPRED is compared with previous study. Encouragingly, JPPRED obtains balanced performance for each type of J-proteins, which is significantly superior to that of the existing method. It is anticipated that JPPRED can be a potential candidate for J-protein prediction.

Author: Lina Zhang, Chengjin Zhang, Rui Gao, and Runtao Yang



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