Document Type
Article
Publication Date
10-2018
Keywords
meta strategy, dual threshold, significance voting, decision tree based artificial neural network, protein intrinsic disorder
Digital Object Identifier (DOI)
https://doi.org/10.3390/ijms19103052
Abstract
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.
Rights Information
This work is licensed under a Creative Commons Attribution 4.0 License.
Was this content written or created while at USF?
Yes
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). Molecular Biosciences Faculty Publications. 41.
https://digitalcommons.usf.edu/bcm_facpub/41
