Predicting post-translational lysine acetylation using support vector machines

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Predicting post-translational lysine acetylation using support vector machines. / Gnad, Florian; Ren, Shubin; Choudhary, Chunaram; Cox, Jürgen; Mann, Matthias.

In: Bioinformatics, Vol. 26, No. 13, 01.07.2010, p. 1666-8.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Gnad, F, Ren, S, Choudhary, C, Cox, J & Mann, M 2010, 'Predicting post-translational lysine acetylation using support vector machines', Bioinformatics, vol. 26, no. 13, pp. 1666-8. https://doi.org/10.1093/bioinformatics/btq260

APA

Gnad, F., Ren, S., Choudhary, C., Cox, J., & Mann, M. (2010). Predicting post-translational lysine acetylation using support vector machines. Bioinformatics, 26(13), 1666-8. https://doi.org/10.1093/bioinformatics/btq260

Vancouver

Gnad F, Ren S, Choudhary C, Cox J, Mann M. Predicting post-translational lysine acetylation using support vector machines. Bioinformatics. 2010 Jul 1;26(13):1666-8. https://doi.org/10.1093/bioinformatics/btq260

Author

Gnad, Florian ; Ren, Shubin ; Choudhary, Chunaram ; Cox, Jürgen ; Mann, Matthias. / Predicting post-translational lysine acetylation using support vector machines. In: Bioinformatics. 2010 ; Vol. 26, No. 13. pp. 1666-8.

Bibtex

@article{f41f09072a6b4ee895507ddef62f7662,
title = "Predicting post-translational lysine acetylation using support vector machines",
abstract = "Lysine acetylation is a post-translational protein modification and a primary regulatory mechanism that controls many cell signaling processes. Lysine acetylation sites are recognized by acetyltransferases and deacetylases through sequence patterns (motifs). Recently, we used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico prediction of acetylated lysine residues.",
keywords = "Acetylation, Algorithms, Humans, Lysine, Protein Processing, Post-Translational, Proteins",
author = "Florian Gnad and Shubin Ren and Chunaram Choudhary and J{\"u}rgen Cox and Matthias Mann",
year = "2010",
month = "7",
day = "1",
doi = "10.1093/bioinformatics/btq260",
language = "English",
volume = "26",
pages = "1666--8",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "13",

}

RIS

TY - JOUR

T1 - Predicting post-translational lysine acetylation using support vector machines

AU - Gnad, Florian

AU - Ren, Shubin

AU - Choudhary, Chunaram

AU - Cox, Jürgen

AU - Mann, Matthias

PY - 2010/7/1

Y1 - 2010/7/1

N2 - Lysine acetylation is a post-translational protein modification and a primary regulatory mechanism that controls many cell signaling processes. Lysine acetylation sites are recognized by acetyltransferases and deacetylases through sequence patterns (motifs). Recently, we used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico prediction of acetylated lysine residues.

AB - Lysine acetylation is a post-translational protein modification and a primary regulatory mechanism that controls many cell signaling processes. Lysine acetylation sites are recognized by acetyltransferases and deacetylases through sequence patterns (motifs). Recently, we used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico prediction of acetylated lysine residues.

KW - Acetylation

KW - Algorithms

KW - Humans

KW - Lysine

KW - Protein Processing, Post-Translational

KW - Proteins

U2 - 10.1093/bioinformatics/btq260

DO - 10.1093/bioinformatics/btq260

M3 - Journal article

VL - 26

SP - 1666

EP - 1668

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 13

ER -

ID: 33748612