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BioMed Research International
Volume 2016 (2016), Article ID 3281590, 12 pages
http://dx.doi.org/10.1155/2016/3281590
Research Article

RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest

1Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
2Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
3Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA

Received 2 October 2015; Revised 13 January 2016; Accepted 31 January 2016

Academic Editor: Zhirong Sun

Copyright © 2016 Hamid D. Ismail 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

The supplementary Tables (1, 2, 3, 4, and 5) show the accuracy, precision, sensitivity, specificity, F1-score, Matthew’s correlation coefficient (MCC), and the area under the curve based on 10-fold cross validation for models trained with sequences of window-sizes of 7, 11, 15, 19, and 21, respectively.

  1. Supplementary Material