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2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2015)
Washington, DC, USA
Nov. 9, 2015 to Nov. 12, 2015
ISBN: 978-1-4673-6798-1
pp: 135-140
Ahoi Jones , Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States of America
Hamid Ismail , Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States of America
Jung H. Kim , Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States of America
Robert Newman , Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States of America
B KC Dukka , Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States of America
ABSTRACT
It is estimated that about 30% of the proteins in the human proteome are regulated by phosphorylation. In recent years, phosphorylation site prediction has been investigated in the field of bioinformatics. This has become necessary due to the challenges associated with experimental methods. Previously, we developed a random forest-based method, termed Random Forest-based Phosphosite predictor (RF-Phos 1.0), to predict phosphorylation sites in proteins given only the amino acid sequence of a protein as input. Here, we report an improved version of this method, termed RF-Phos 1.1 that employs additional sequence-driven features to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation analysis and an independent dataset, RF-Phos 1.1 performs comparably to or better than other existing phosphosite prediction methods, such as PhosphoSVM, GPS2.1 and Musite.
INDEX TERMS
Protein Functional prediction, Phosphorylation site prediction, Random Forest
CITATION
Ahoi Jones, Hamid Ismail, Jung H. Kim, Robert Newman, B KC Dukka, "RF-Phos: Random forest-based prediction of phosphorylation sites", 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), vol. 00, no. , pp. 135-140, 2015, doi:10.1109/BIBM.2015.7359670
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