RF-Phos: Random forest-based prediction of phosphorylation sites

Ahoi Jones; Hamid Ismail; Jung H. Kim; Robert H Newman; K. C. Dukkab

doi:10.1109/BIBM.2015.7359670

RF-Phos: Random forest-based prediction of phosphorylation sites

Ahoi Jones
, Hamid Ismail
, Jung H. Kim
, Robert H Newman
, K. C. Dukkab

Industrial and systems engineering with North Carolina A&T State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editors	lng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
Place of Publication	usa
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	135-140
Number of pages	6
ISBN (Electronic)	9781467367981
DOIs	https://doi.org/10.1109/BIBM.2015.7359670
State	Published - Dec 16 2015
Event	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States Duration: Nov 9 2015 → Nov 12 2015

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Country/Territory	United States
City	Washington
Period	11/9/15 → 11/12/15

Keywords

Phosphorylation site prediction
Protein Functional prediction
Random Forest

Access to Document

10.1109/BIBM.2015.7359670

Cite this

Jones, A., Ismail, H., Kim, J. H., Newman, R. H., & Dukkab, K. C. (2015). RF-Phos: Random forest-based prediction of phosphorylation sites. In L. M. Schapranow, J. Zhou, X. T. Hu, B. Ma, S. Rajasekaran, S. Miyano, I. Yoo, B. Pierce, A. Shehu, V. K. Gombar, B. Chen, V. Pai, & J. Huan (Eds.), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 135-140). Article 7359670 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359670

Jones, Ahoi ; Ismail, Hamid ; Kim, Jung H. et al. / RF-Phos: Random forest-based prediction of phosphorylation sites. Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. editor / lng. Matthieu Schapranow ; Jiayu Zhou ; Xiaohua Tony Hu ; Bin Ma ; Sanguthevar Rajasekaran ; Satoru Miyano ; Illhoi Yoo ; Brian Pierce ; Amarda Shehu ; Vijay K. Gombar ; Brian Chen ; Vinay Pai ; Jun Huan. usa : Institute of Electrical and Electronics Engineers Inc., 2015. pp. 135-140

@inproceedings{554cb70bf76241968e3416368113bd6f,

title = "RF-Phos: Random forest-based prediction of phosphorylation sites",

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.",

keywords = "Phosphorylation site prediction, Protein Functional prediction, Random Forest",

author = "Ahoi Jones and Hamid Ismail and Kim, \{Jung H.\} and Newman, \{Robert H\} and Dukkab, \{K. C.\}",

year = "2015",

month = dec,

day = "16",

doi = "10.1109/BIBM.2015.7359670",

language = "English",

pages = "135--140",

editor = "Schapranow, \{lng. Matthieu\} and Jiayu Zhou and Hu, \{Xiaohua Tony\} and Bin Ma and Sanguthevar Rajasekaran and Satoru Miyano and Illhoi Yoo and Brian Pierce and Amarda Shehu and Gombar, \{Vijay K.\} and Brian Chen and Vinay Pai and Jun Huan",

booktitle = "Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

}

Jones, A, Ismail, H, Kim, JH, Newman, RH & Dukkab, KC 2015, RF-Phos: Random forest-based prediction of phosphorylation sites. in LM Schapranow, J Zhou, XT Hu, B Ma, S Rajasekaran, S Miyano, I Yoo, B Pierce, A Shehu, VK Gombar, B Chen, V Pai & J Huan (eds), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015., 7359670, Institute of Electrical and Electronics Engineers Inc., usa, pp. 135-140, IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Washington, United States, 11/9/15. https://doi.org/10.1109/BIBM.2015.7359670

RF-Phos: Random forest-based prediction of phosphorylation sites. / Jones, Ahoi; Ismail, Hamid; Kim, Jung H. et al.
Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. ed. / lng. Matthieu Schapranow; Jiayu Zhou; Xiaohua Tony Hu; Bin Ma; Sanguthevar Rajasekaran; Satoru Miyano; Illhoi Yoo; Brian Pierce; Amarda Shehu; Vijay K. Gombar; Brian Chen; Vinay Pai; Jun Huan. usa: Institute of Electrical and Electronics Engineers Inc., 2015. p. 135-140 7359670.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - RF-Phos: Random forest-based prediction of phosphorylation sites

AU - Jones, Ahoi

AU - Ismail, Hamid

AU - Kim, Jung H.

AU - Newman, Robert H

AU - Dukkab, K. C.

PY - 2015/12/16

Y1 - 2015/12/16

N2 - 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.

AB - 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.

KW - Phosphorylation site prediction

KW - Protein Functional prediction

KW - Random Forest

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BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

A2 - Schapranow, lng. Matthieu

A2 - Zhou, Jiayu

A2 - Hu, Xiaohua Tony

A2 - Ma, Bin

A2 - Rajasekaran, Sanguthevar

A2 - Miyano, Satoru

A2 - Yoo, Illhoi

A2 - Pierce, Brian

A2 - Shehu, Amarda

A2 - Gombar, Vijay K.

A2 - Chen, Brian

A2 - Pai, Vinay

A2 - Huan, Jun

PB - Institute of Electrical and Electronics Engineers Inc.

CY - usa

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ER -

Jones A, Ismail H, Kim JH, Newman RH, Dukkab KC. RF-Phos: Random forest-based prediction of phosphorylation sites. In Schapranow LM, Zhou J, Hu XT, Ma B, Rajasekaran S, Miyano S, Yoo I, Pierce B, Shehu A, Gombar VK, Chen B, Pai V, Huan J, editors, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. usa: Institute of Electrical and Electronics Engineers Inc. 2015. p. 135-140. 7359670 doi: 10.1109/BIBM.2015.7359670

RF-Phos: Random forest-based prediction of phosphorylation sites

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