NDLI: Methods for improving protein disorder prediction

Content Provider	IEEE Xplore Digital Library
Author	Vucetic, S. Radivojac, P. Obradovic, Z. Brown, C.J. Dunker, A.K.
Copyright Year	2001
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA (Vucetic, S.)
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 than 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.
Sponsorship	Int. Neural Network Soc.
Starting Page	2718
Ending Page	2723
File Size	656718
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.938802
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Amino acids Neural networks Protein engineering Protein sequence Sequences Databases Computer science Biochemistry Biophysics Information science
Content Type	Text
Resource Type	Article

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