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Open AccessArticle

Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs

by Bi Zhao and Bin Xue *
Department of Cell Biology, Microbiology and Molecular Biology, School of Natural Sciences and Mathematics, College of Arts and Sciences, University of South Florida, Tampa, FL 33620, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2018, 19(10), 3052; https://doi.org/10.3390/ijms19103052
Received: 20 August 2018 / Revised: 24 September 2018 / Accepted: 4 October 2018 / Published: 7 October 2018
Using computational techniques to identify intrinsically disordered residues is practical and effective in biological studies. Therefore, designing novel high-accuracy strategies is always preferable when existing strategies have a lot of room for improvement. Among many possibilities, a meta-strategy that integrates the results of multiple individual predictors has been broadly used to improve the overall performance of predictors. Nonetheless, a simple and direct integration of individual predictors may not effectively improve the performance. In this project, dual-threshold two-step significance voting and neural networks were used to integrate the predictive results of four individual predictors, including: DisEMBL, IUPred, VSL2, and ESpritz. The new meta-strategy has improved the prediction performance of intrinsically disordered residues significantly, compared to all four individual predictors and another four recently-designed predictors. The improvement was validated using five-fold cross-validation and in independent test datasets. View Full-Text
Keywords: meta strategy; dual threshold; significance voting; decision tree based artificial neural network; protein intrinsic disorder meta strategy; dual threshold; significance voting; decision tree based artificial neural network; protein intrinsic disorder
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Zhao, B.; Xue, B. Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs. Int. J. Mol. Sci. 2018, 19, 3052.

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