meta strategy, dual threshold, significance voting, decision tree based artificial neural network, protein intrinsic disorder
Digital Object Identifier (DOI)
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.
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Citation / Publisher Attribution
International Journal of Molecular Sciences, v. 19, issue 10, art. 3052
Scholar Commons Citation
Zhao, Bi and Xue, Bin, "Decision-Tree Based Meta-Strategy Improved Accuracy of Disorder Prediction and Identified Novel Disordered Residues Inside Binding Motifs" (2018). Cell Biology, Microbiology, and Molecular Biology Faculty Publications. 41.