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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: unimas 2016
Subjects:
Q Science (General)
QM Human anatomy
Online Access:http://ir.unimas.my/id/eprint/20988/3/Fong%20Pui.pdf
http://ir.unimas.my/id/eprint/20988/
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spelling my.unimas.ir.209882023-11-14T01:26:19Z http://ir.unimas.my/id/eprint/20988/ DNA enhancer prediction using machine learning techniques with novel feature representation 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 NonPeerReviewed text en http://ir.unimas.my/id/eprint/20988/3/Fong%20Pui.pdf Fong, Pui Kwan (2016) DNA enhancer prediction using machine learning techniques with novel feature representation. PhD thesis, UNIMAS.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
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
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
publisher unimas
publishDate 2016
url http://ir.unimas.my/id/eprint/20988/3/Fong%20Pui.pdf
http://ir.unimas.my/id/eprint/20988/
_version_ 1783883499309105152
score 13.211869

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