Prediction of biological functions by histone modification patterns profiling

Yiou Xiao; Kishan G. Mehrotra; Chilukuri K Mohan; Pratibha Choudhary; Ramesh Raina

Prediction of biological functions by histone modification patterns profiling

Yiou Xiao, Kishan G. Mehrotra, Chilukuri K Mohan, Pratibha Choudhary, Ramesh Raina

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

Abstract

Histone modifications provide an important layer of gene regulation in eukaryotes. In this paper, we propose an approach that identifies the histone modification patterns most relevant for specific biological functions, such as flowering in plants. We first propose a new pattern scoring method, which evaluates the importance of each combinatorial pattern of histone modifications; this is used along with logistic regression, Support Vector Machines, and naive Bayesian classifier algorithms to predict gene functions. This approach is shown to be successful in inferring significant patterns verified by independent gene function data, outperforming other pattern scores used in current histone modification analysis research.

Original language	English (US)
Title of host publication	Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
Publisher	The International Society for Computers and Their Applications (ISCA)
Pages	217-222
Number of pages	6
ISBN (Electronic)	9781943436071
State	Published - 2017
Event	9th International Conference on Bioinformatics and Computational Biology, BICOB 2017 - Honolulu, United States Duration: Mar 20 2017 → Mar 22 2017

Other

Other	9th International Conference on Bioinformatics and Computational Biology, BICOB 2017
Country	United States
City	Honolulu
Period	3/20/17 → 3/22/17

Keywords

Gene expression
Histone modification
Predicting biological function

ASJC Scopus subject areas

Biomedical Engineering
Health Information Management

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Prediction of biological functions by histone modification patterns profiling. / Xiao, Yiou; Mehrotra, Kishan G.; Mohan, Chilukuri K; Choudhary, Pratibha; Raina, Ramesh.

Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017. The International Society for Computers and Their Applications (ISCA), 2017. p. 217-222.

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

Xiao, Y, Mehrotra, KG, Mohan, CK, Choudhary, P & Raina, R 2017, Prediction of biological functions by histone modification patterns profiling. in Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017. The International Society for Computers and Their Applications (ISCA), pp. 217-222, 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017, Honolulu, United States, 3/20/17.

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abstract = "Histone modifications provide an important layer of gene regulation in eukaryotes. In this paper, we propose an approach that identifies the histone modification patterns most relevant for specific biological functions, such as flowering in plants. We first propose a new pattern scoring method, which evaluates the importance of each combinatorial pattern of histone modifications; this is used along with logistic regression, Support Vector Machines, and naive Bayesian classifier algorithms to predict gene functions. This approach is shown to be successful in inferring significant patterns verified by independent gene function data, outperforming other pattern scores used in current histone modification analysis research.",

keywords = "Gene expression, Histone modification, Predicting biological function",

author = "Yiou Xiao and Mehrotra, {Kishan G.} and Mohan, {Chilukuri K} and Pratibha Choudhary and Ramesh Raina",

year = "2017",

language = "English (US)",

pages = "217--222",

booktitle = "Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017",

publisher = "The International Society for Computers and Their Applications (ISCA)",

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AU - Mehrotra, Kishan G.

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AU - Choudhary, Pratibha

AU - Raina, Ramesh

PY - 2017

Y1 - 2017

N2 - Histone modifications provide an important layer of gene regulation in eukaryotes. In this paper, we propose an approach that identifies the histone modification patterns most relevant for specific biological functions, such as flowering in plants. We first propose a new pattern scoring method, which evaluates the importance of each combinatorial pattern of histone modifications; this is used along with logistic regression, Support Vector Machines, and naive Bayesian classifier algorithms to predict gene functions. This approach is shown to be successful in inferring significant patterns verified by independent gene function data, outperforming other pattern scores used in current histone modification analysis research.

AB - Histone modifications provide an important layer of gene regulation in eukaryotes. In this paper, we propose an approach that identifies the histone modification patterns most relevant for specific biological functions, such as flowering in plants. We first propose a new pattern scoring method, which evaluates the importance of each combinatorial pattern of histone modifications; this is used along with logistic regression, Support Vector Machines, and naive Bayesian classifier algorithms to predict gene functions. This approach is shown to be successful in inferring significant patterns verified by independent gene function data, outperforming other pattern scores used in current histone modification analysis research.

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KW - Histone modification

KW - Predicting biological function

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M3 - Conference contribution

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BT - Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017

PB - The International Society for Computers and Their Applications (ISCA)

T2 - 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017

Y2 - 20 March 2017 through 22 March 2017

ER -

Prediction of biological functions by histone modification patterns profiling

Abstract

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Keywords

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