Methods for improving protein disorder prediction

S. Vucetic, P. Radivojac, Z. Obradovic, C. J. Brown, A. K. Dunker

Research output: Contribution to conferencePaper

14 Scopus citations

Abstract

In this paper we propose several methods for improving prediction of protein disorder. These include attribute construction from protein sequence, choice of classifier and postprocessing. While ensembles of neural networks achieved the higher accuracy, the difference as compared to logistic regression classifiers was smaller then 1%. Bagging of neural networks, where moving averages over windows of length 61 were used for attribute construction, combined with postprocessing by averaging predictions over windows of length 81 resulted in 82.6% accuracy for a larger set of ordered and disordered proteins than used previously. This result was a significant improvement over previous methodology, which gave an accuracy of 70.2%. Moreover, unlike the previous methodology, the modified attribute construction allowed prediction at protein ends.

Original languageEnglish (US)
Pages2718-2723
Number of pages6
StatePublished - Jan 1 2001
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period7/15/017/19/01

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Vucetic, S., Radivojac, P., Obradovic, Z., Brown, C. J., & Dunker, A. K. (2001). Methods for improving protein disorder prediction. 2718-2723. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.