TITLE:
Predicting Βeta-Turns and Βeta-Turn Types Using a Novel Over-Sampling Approach
AUTHORS:
Lan Anh T. Nguyen, Xuan Tho Dang, Tu Kien T. Le, Thammakorn Saethang, Vu Anh Tran, Duc Luu Ngo, Sergey Gavrilov, Ngoc Giang Nguyen, Mamoru Kubo, Yoichi Yamada, Kenji Satou
KEYWORDS:
Beta-Turns, Beta-Turn Types, Class-Imbalance, Over-Sampling
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.7 No.11,
September
18,
2014
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.