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

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Citation / Publisher Attribution

International Journal of Molecular Sciences, v. 19, issue 10, art. 3052

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