Comprehensive assessment of DNA content feature using machine learning approach

Transcription Factor Proteins-DNA interactions play the key role in gene regulation. Identification of the regulatory elements or motifs bound by transcription factor proteins is critical to understand the gene regulatory network, diseases, and for medical benefit. Computational motif analysis, spec...

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Main Author:	Sina, Nazeri
Format:	Thesis
Language:	English
Published:	2016
Subjects:	H Social Sciences (General) QH426 Genetics QM Human anatomy
Online Access:	http://ir.unimas.my/id/eprint/20987/1/Sina%20Nazeri.pdf
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id	my-unimas-ir.20987
record_format	uketd_dc
spelling	my-unimas-ir.209872023-07-28T08:07:00Z Comprehensive assessment of DNA content feature using machine learning approach 2016 Sina, Nazeri H Social Sciences (General) QH426 Genetics QM Human anatomy Transcription Factor Proteins-DNA interactions play the key role in gene regulation. Identification of the regulatory elements or motifs bound by transcription factor proteins is critical to understand the gene regulatory network, diseases, and for medical benefit. Computational motif analysis, specifically the distal regulatory elements –enhancers– is notoriously difficult. Firstly, there are limited choices of features associated with it for machine learning task. Secondly, the discriminative feature that describes enhancer regions are ill-understood and no prior knowledge can be used in the design of recognition system. Lastly, different development stages and different cell lines activate different subset of enhancers which complicate computational methods of making conclusive results on the discriminative feature set that is used to model the active enhancers. Epigenetic and chromatin landmarks have been employed with great success to infer locations of enhancer regions as their locations have high correlation with enhancer regions. K-mer feature representation is one prominent approach for DNA content representation. unimas 2016 Thesis http://ir.unimas.my/id/eprint/20987/ http://ir.unimas.my/id/eprint/20987/1/Sina%20Nazeri.pdf text en validuser masters UNIMAS
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	H Social Sciences (General) QH426 Genetics QM Human anatomy
spellingShingle	H Social Sciences (General) QH426 Genetics QM Human anatomy Sina, Nazeri Comprehensive assessment of DNA content feature using machine learning approach
description	Transcription Factor Proteins-DNA interactions play the key role in gene regulation. Identification of the regulatory elements or motifs bound by transcription factor proteins is critical to understand the gene regulatory network, diseases, and for medical benefit. Computational motif analysis, specifically the distal regulatory elements –enhancers– is notoriously difficult. Firstly, there are limited choices of features associated with it for machine learning task. Secondly, the discriminative feature that describes enhancer regions are ill-understood and no prior knowledge can be used in the design of recognition system. Lastly, different development stages and different cell lines activate different subset of enhancers which complicate computational methods of making conclusive results on the discriminative feature set that is used to model the active enhancers. Epigenetic and chromatin landmarks have been employed with great success to infer locations of enhancer regions as their locations have high correlation with enhancer regions. K-mer feature representation is one prominent approach for DNA content representation.
format	Thesis
qualification_level	Master's degree
author	Sina, Nazeri
author_facet	Sina, Nazeri
author_sort	Sina, Nazeri
title	Comprehensive assessment of DNA content feature using machine learning approach
title_short	Comprehensive assessment of DNA content feature using machine learning approach
title_full	Comprehensive assessment of DNA content feature using machine learning approach
title_fullStr	Comprehensive assessment of DNA content feature using machine learning approach
title_full_unstemmed	Comprehensive assessment of DNA content feature using machine learning approach
title_sort	comprehensive assessment of dna content feature using machine learning approach
granting_institution	UNIMAS
publishDate	2016
url	http://ir.unimas.my/id/eprint/20987/1/Sina%20Nazeri.pdf
_version_	1783728214911221760

Comprehensive assessment of DNA content feature using machine learning approach

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