LAceP: lysine acetylation site prediction using logistic regression classifiers.

<h4>Background</h4>Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming an...

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Autores principales:	Ting Hou, Guangyong Zheng, Pingyu Zhang, Jia Jia, Jing Li, Lu Xie, Chaochun Wei, Yixue Li
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/af20b4b99c3a4ec6b8b0d73c79b01576
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spelling	oai:doaj.org-article:af20b4b99c3a4ec6b8b0d73c79b015762021-11-18T08:31:39ZLAceP: lysine acetylation site prediction using logistic regression classifiers.1932-620310.1371/journal.pone.0089575https://doaj.org/article/af20b4b99c3a4ec6b8b0d73c79b015762014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24586884/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding.<h4>Result</h4>In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets.<h4>Conclusion</h4>LAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/.Ting HouGuangyong ZhengPingyu ZhangJia JiaJing LiLu XieChaochun WeiYixue LiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 2, p e89575 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ting Hou Guangyong Zheng Pingyu Zhang Jia Jia Jing Li Lu Xie Chaochun Wei Yixue Li LAceP: lysine acetylation site prediction using logistic regression classifiers.
description	<h4>Background</h4>Lysine acetylation is a crucial type of protein post-translational modification, which is involved in many important cellular processes and serious diseases. However, identification of protein acetylated sites through traditional experiment methods is time-consuming and laborious. Those methods are not suitable to identify a large number of acetylated sites quickly. Therefore, computational methods are still very valuable to accelerate lysine acetylated site finding.<h4>Result</h4>In this study, many biological characteristics of acetylated sites have been investigated, such as the amino acid sequence around the acetylated sites, the physicochemical property of the amino acids and the transition probability of adjacent amino acids. A logistic regression method was then utilized to integrate these information for generating a novel lysine acetylation prediction system named LAceP. When compared with existing methods, LAceP overwhelms most of state-of-the-art methods. Especially, LAceP has a more balanced prediction capability for positive and negative datasets.<h4>Conclusion</h4>LAceP can integrate different biological features to predict lysine acetylation with high accuracy. An online web server is freely available at http://www.scbit.org/iPTM/.
format	article
author	Ting Hou Guangyong Zheng Pingyu Zhang Jia Jia Jing Li Lu Xie Chaochun Wei Yixue Li
author_facet	Ting Hou Guangyong Zheng Pingyu Zhang Jia Jia Jing Li Lu Xie Chaochun Wei Yixue Li
author_sort	Ting Hou
title	LAceP: lysine acetylation site prediction using logistic regression classifiers.
title_short	LAceP: lysine acetylation site prediction using logistic regression classifiers.
title_full	LAceP: lysine acetylation site prediction using logistic regression classifiers.
title_fullStr	LAceP: lysine acetylation site prediction using logistic regression classifiers.
title_full_unstemmed	LAceP: lysine acetylation site prediction using logistic regression classifiers.
title_sort	lacep: lysine acetylation site prediction using logistic regression classifiers.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/af20b4b99c3a4ec6b8b0d73c79b01576
work_keys_str_mv	AT tinghou laceplysineacetylationsitepredictionusinglogisticregressionclassifiers AT guangyongzheng laceplysineacetylationsitepredictionusinglogisticregressionclassifiers AT pingyuzhang laceplysineacetylationsitepredictionusinglogisticregressionclassifiers AT jiajia laceplysineacetylationsitepredictionusinglogisticregressionclassifiers AT jingli laceplysineacetylationsitepredictionusinglogisticregressionclassifiers AT luxie laceplysineacetylationsitepredictionusinglogisticregressionclassifiers AT chaochunwei laceplysineacetylationsitepredictionusinglogisticregressionclassifiers AT yixueli laceplysineacetylationsitepredictionusinglogisticregressionclassifiers
_version_	1718421703855439872

LAceP: lysine acetylation site prediction using logistic regression classifiers.

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