Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators

The completion of the human genome project in the last decade has generated a strong demand in computational analysis techniques in order to fully exploit the acquired human genome database. The human genome project generated a perplexing mass of genetic data which necessitates automatic genome anno...

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Main Authors:	Htike@Muhammad Yusof, Zaw Zaw, Win, Shoon Lei
Format:	Article
Language:	English
Published:	Elsevier Ltd. 2013
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/34337/ http://irep.iium.edu.my/34337/ http://irep.iium.edu.my/34337/ http://irep.iium.edu.my/34337/4/Recognition_of_promoters_in_DNA_sequences_%28Paper_2%29.pdf

id	iium-34337
recordtype	eprints
spelling	iium-343372015-06-01T03:29:28Z http://irep.iium.edu.my/34337/ Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators Htike@Muhammad Yusof, Zaw Zaw Win, Shoon Lei Q Science (General) The completion of the human genome project in the last decade has generated a strong demand in computational analysis techniques in order to fully exploit the acquired human genome database. The human genome project generated a perplexing mass of genetic data which necessitates automatic genome annotation. There is a growing interest in the process of gene finding and gene recognition from DNA sequences. In genetics, a promoter is a segment of a DNA that marks the starting point of transcription of a particular gene. Therefore, recognizing promoters is a one step towards gene finding in DNA sequences. Promoters also play a fundamental role in many other vital cellular processes. Aberrant promoters can cause a wide range of diseases including cancers. This paper describes a state-of-the-art machine learning based approach called weightily averaged one-dependence estimators to tackle the problem of recognizing promoters in genetic sequences. To lower the computational complexity and to increase the generalization capability of the system, we employ an entropy-based feature extraction approach to select relevant nucleotides that are directly responsible for promoter recognition. We carried out experiments on a dataset extracted from the biological literature for a proof-of-concept. The proposed system has achieved an accuracy of 97.17 % in classifying promoters. The experimental results demonstrate the efficacy of our framework and encourage us to extend the framework to recognize promoter sequences in various species of higher eukaryotes. Elsevier Ltd. 2013-11-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/34337/4/Recognition_of_promoters_in_DNA_sequences_%28Paper_2%29.pdf Htike@Muhammad Yusof, Zaw Zaw and Win, Shoon Lei (2013) Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators. Procedia Computer Science, 23. pp. 60-67. ISSN 1877-0509 http://www.sciencedirect.com/science/article/pii/S1877050913011447 10.1016/j.procs.2013.10.009
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Htike@Muhammad Yusof, Zaw Zaw Win, Shoon Lei Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators
description	The completion of the human genome project in the last decade has generated a strong demand in computational analysis techniques in order to fully exploit the acquired human genome database. The human genome project generated a perplexing mass of genetic data which necessitates automatic genome annotation. There is a growing interest in the process of gene finding and gene recognition from DNA sequences. In genetics, a promoter is a segment of a DNA that marks the starting point of transcription of a particular gene. Therefore, recognizing promoters is a one step towards gene finding in DNA sequences. Promoters also play a fundamental role in many other vital cellular processes. Aberrant promoters can cause a wide range of diseases including cancers. This paper describes a state-of-the-art machine learning based approach called weightily averaged one-dependence estimators to tackle the problem of recognizing promoters in genetic sequences. To lower the computational complexity and to increase the generalization capability of the system, we employ an entropy-based feature extraction approach to select relevant nucleotides that are directly responsible for promoter recognition. We carried out experiments on a dataset extracted from the biological literature for a proof-of-concept. The proposed system has achieved an accuracy of 97.17 % in classifying promoters. The experimental results demonstrate the efficacy of our framework and encourage us to extend the framework to recognize promoter sequences in various species of higher eukaryotes.
format	Article
author	Htike@Muhammad Yusof, Zaw Zaw Win, Shoon Lei
author_facet	Htike@Muhammad Yusof, Zaw Zaw Win, Shoon Lei
author_sort	Htike@Muhammad Yusof, Zaw Zaw
title	Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators
title_short	Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators
title_full	Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators
title_fullStr	Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators
title_full_unstemmed	Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators
title_sort	recognition of promoters in dna sequences using weightily averaged one-dependence estimators
publisher	Elsevier Ltd.
publishDate	2013
url	http://irep.iium.edu.my/34337/ http://irep.iium.edu.my/34337/ http://irep.iium.edu.my/34337/ http://irep.iium.edu.my/34337/4/Recognition_of_promoters_in_DNA_sequences_%28Paper_2%29.pdf
first_indexed	2023-09-18T20:49:30Z
last_indexed	2023-09-18T20:49:30Z
_version_	1777409899338137600

Recognition of promoters in DNA sequences using weightily averaged one-dependence estimators

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