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.
Highlights
Secondary structure that includes regular and irregular patterns is important in protein folding study because it can be a building block of three-dimensional structures
We evaluate the performance of the novel over-sampling algorithm on the five other datasets from UCI Machine Learning Repository
In addition to three standard benchmark datasets above, we evaluated the performance of our novel over-sampling algorithm Flexible Over-Sampling Technique (FOST) on the five datasets which were obtained from UCI Machine Learning Repository [34]: Haberman’s Survival, Pima Indian Diabetes, Glass Identification, Landsat Satellite, and Yeast
Summary
Secondary structure that includes regular and irregular patterns is important in protein folding study because it can be a building block of three-dimensional structures. The regular structures, which are sequences of residues with repeating φ and ψ values, are classified in α-helix and β-strand. While this group is well defined, the irregular structures that cover 50% of remaining protein residues are classified as coils. Tight turn is the most important one from the viewpoint of protein structure as well as function [1]. Tight turns are categorized as δ-, γ-, β-, α-, and π-turns according to the number of consecutive residues in the turn
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