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Predicting Βeta-Turns and Βeta-Turn Types Using a Novel Over-Sampling Approach
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
Department of Computer Science, Hue University of Education, Hue, Vietnam
- 1 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 2 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 3 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 4 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 5 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 6 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 7 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 8 Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
- 9 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 10 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 11 Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan
- 12 Department of Computer Science, Hue University of Education, Hue, Vietnam
Journal of Biomedical Science and Engineering·Volume 07 (2014)·Pages 927–940·Published 25 August 2014·DOI10.4236/jbise.2014.711090
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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.
KeywordsBeta-TurnsBeta-Turn TypesClass-ImbalanceOver-Sampling
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