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• W2038020595 abstract "Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E-H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between α/β class and α + β class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate α/β and α + β classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html." @default.
• W2038020595 created "2016-06-24" @default.
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• W2038020595 date "2012-05-01" @default.
• W2038020595 modified "2023-09-22" @default.
• W2038020595 title "A novel protein structural classes prediction method based on predicted secondary structure" @default.
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• W2038020595 doi "https://doi.org/10.1016/j.biochi.2012.01.022" @default.
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