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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 857325, 9 pages
Research Article

Prediction of “Aggregation-Prone” Peptides with Hybrid Classification Approach

1School of Physical Education, Northeast Normal University, Changchun 130117, China
2School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China

Received 23 July 2014; Revised 29 September 2014; Accepted 29 September 2014

Academic Editor: Shifei Ding

Copyright © 2015 Bo Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

Text S1: The dataset for prediction of aggregation prone. All datasets are consisting of the 33 proteins and their sites information.

Text S2: The prediction result comparison with the AMYLPRED and AMYLPRED2. The prediction result of aggregation prone for Aggre Easy, Aggre Balance, AMYLPRED and AMYLPRED2, by comparison. Simply, we remove the single prediction positive site, and, in the future, we will propose the window redirection operator to improve the prediction performance.

  1. Supplementary Materials