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2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (2015)
Miami, FL, USA
Oct. 15, 2015 to Oct. 17, 2015
ISBN: 978-1-4673-9662-2
pp: 1-6
Hamid D. Ismail , Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States
Ahoi Jones , Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States
Jung H. Kim , Department of Electrical and Computer Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States
Robert H. Newman , Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States
KC Dukka B. , Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, 27411, United States
ABSTRACT
Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 1.0 (RF-Phos 1.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 1.0, which uses random forest classifiers to integrate various sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 1.0 compares favorably to other existing phosphosite prediction methods, such as PhosphoSVM, GPS2.1 and Musite.
INDEX TERMS
Protein Functional prediction, Phosphorylation site prediction, Random Forest
CITATION
Hamid D. Ismail, Ahoi Jones, Jung H. Kim, Robert H. Newman, KC Dukka B., "Phosphorylation sites prediction using Random Forest", 2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), vol. 00, no. , pp. 1-6, 2015, doi:10.1109/ICCABS.2015.7344726
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