DNA enhancer prediction using machine learning techniques with novel feature representation

Identification of regulatory elements particularly enhancer region plays an important role in comprehending the regulation of gene expression. Current computational enhancer prediction tools are centred at Support Vector Machine (SVM) utilizing sequence content feature—the k-mer. While content featu...

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Bibliographic Details
Main Author:	Fong, Pui Kwan
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Q Science (General) QM Human anatomy
Online Access:	http://ir.unimas.my/id/eprint/20988/3/Fong%20Pui.pdf
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id	my-unimas-ir.20988
record_format	uketd_dc
spelling	my-unimas-ir.209882023-11-14T01:26:19Z DNA enhancer prediction using machine learning techniques with novel feature representation 2016 Fong, Pui Kwan Q Science (General) QM Human anatomy Identification of regulatory elements particularly enhancer region plays an important role in comprehending the regulation of gene expression. Current computational enhancer prediction tools are centred at Support Vector Machine (SVM) utilizing sequence content feature—the k-mer. While content feature is shown to be promising, it suffers from several critical weaknesses such as: 1) features associated with enhancer regions are ill-defined and poorly understood. The content feature is unable to represent the complex properties of deoxyribonucleic acid (DNA) sequences; 2) the k-mer feature represents only the global property of DNA sequences but not the localized property; and 3) lack of feature extraction, generation and selection techniques in the algorithm design. This dissertation aims to develop novel feature representations of histone DNA sequences which are associated with enhancer locations. Technical contributions of this study are: 1) complex tree-feature modelling using genetic algorithm (CTreeGA): Automated feature generation framework to capture patterns of interactions among short DNA segments in histone sequences. unimas 2016 Thesis http://ir.unimas.my/id/eprint/20988/ http://ir.unimas.my/id/eprint/20988/3/Fong%20Pui.pdf text en validuser phd doctoral UNIMAS
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	Q Science (General) QM Human anatomy
spellingShingle	Q Science (General) QM Human anatomy Fong, Pui Kwan DNA enhancer prediction using machine learning techniques with novel feature representation
description	Identification of regulatory elements particularly enhancer region plays an important role in comprehending the regulation of gene expression. Current computational enhancer prediction tools are centred at Support Vector Machine (SVM) utilizing sequence content feature—the k-mer. While content feature is shown to be promising, it suffers from several critical weaknesses such as: 1) features associated with enhancer regions are ill-defined and poorly understood. The content feature is unable to represent the complex properties of deoxyribonucleic acid (DNA) sequences; 2) the k-mer feature represents only the global property of DNA sequences but not the localized property; and 3) lack of feature extraction, generation and selection techniques in the algorithm design. This dissertation aims to develop novel feature representations of histone DNA sequences which are associated with enhancer locations. Technical contributions of this study are: 1) complex tree-feature modelling using genetic algorithm (CTreeGA): Automated feature generation framework to capture patterns of interactions among short DNA segments in histone sequences.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Fong, Pui Kwan
author_facet	Fong, Pui Kwan
author_sort	Fong, Pui Kwan
title	DNA enhancer prediction using machine learning techniques with novel feature representation
title_short	DNA enhancer prediction using machine learning techniques with novel feature representation
title_full	DNA enhancer prediction using machine learning techniques with novel feature representation
title_fullStr	DNA enhancer prediction using machine learning techniques with novel feature representation
title_full_unstemmed	DNA enhancer prediction using machine learning techniques with novel feature representation
title_sort	dna enhancer prediction using machine learning techniques with novel feature representation
granting_institution	UNIMAS
publishDate	2016
url	http://ir.unimas.my/id/eprint/20988/3/Fong%20Pui.pdf
_version_	1783728215158685696

DNA enhancer prediction using machine learning techniques with novel feature representation

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