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.
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