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Predicting Βeta-Turns and Βeta-Turn Types Using a Novel Over-Sampling Approach

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DOI: 10.4236/jbise.2014.711090    2,423 Downloads   2,986 Views   Citations

ABSTRACT

β-turn is one of the most important reverse turns because of its role in protein folding. Many computational methods have been studied for predicting β-turns and β-turn types. However, due to the imbalanced dataset, the performance is still inadequate. In this study, we proposed a novel over-sampling technique FOST to deal with the class-imbalance problem. Experimental results on three standard benchmark datasets showed that our method is comparable with state-of-the-art methods. In addition, we applied our algorithm to five benchmark datasets from UCI Machine Learning Repository and achieved significant improvement in G-mean and Sensitivity. It means that our method is also effective for various imbalanced data other than β-turns and β-turn types.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Nguyen, L. , Dang, X. , Le, T. , Saethang, T. , Tran, V. , Ngo, D. , Gavrilov, S. , Nguyen, N. , Kubo, M. , Yamada, Y. and Satou, K. (2014) Predicting Βeta-Turns and Βeta-Turn Types Using a Novel Over-Sampling Approach. Journal of Biomedical Science and Engineering, 7, 927-940. doi: 10.4236/jbise.2014.711090.

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