Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution

DNA is the building block of life, which contains encoded genetic instructions for building living organisms. Because of the fact that proteins are constructed in accordance with the genetic instructions encoded in DNAs, errors in RNA synthesis and translation into proteins can cause genetic disorde...

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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/34320/ http://irep.iium.edu.my/34320/ http://irep.iium.edu.my/34320/ http://irep.iium.edu.my/34320/6/Classification_of_eukaryotic_splice-junction_genetic_sequences.pdf

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spelling	iium-343202015-06-01T03:28:31Z http://irep.iium.edu.my/34320/ Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution Htike@Muhammad Yusof, Zaw Zaw Win, Shoon Lei Q Science (General) DNA is the building block of life, which contains encoded genetic instructions for building living organisms. Because of the fact that proteins are constructed in accordance with the genetic instructions encoded in DNAs, errors in RNA synthesis and translation into proteins can cause genetic disorders. Therefore, understanding and recognizing genetic sequences is one step towards the treatment of these genetic disorders. Since the discovery of DNA, there has been a growing interest in the problem of genetic sequence recognition, motivated by its enormous potential to cure a wide range of genetic disorders. 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. This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of recognizing an important class of genetic sequences known as eukaryotic splice junctions. To lower the computational complexity and to increase the generalization capability of the system, we employ a genetic algorithm to select relevant nucleotides that are directly responsible for splice-junction recognition. We carried out experiments on a dataset extracted from the biological literature. This proposed system has achieved an accuracy of 96.68% in classifying splice-junction genetic sequences. The experimental results demonstrate the efficacy of our framework and encourage us to apply the framework on other types of genetic sequences. Elsevier Ltd. 2013-11-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/34320/6/Classification_of_eukaryotic_splice-junction_genetic_sequences.pdf Htike@Muhammad Yusof, Zaw Zaw and Win, Shoon Lei (2013) Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution. Procedia Computer Science, 23. pp. 36-43. ISSN 1877-0509 http://www.sciencedirect.com/science/article/pii/S1877050913011411 10.1016/j.procs.2013.10.006
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 Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
description	DNA is the building block of life, which contains encoded genetic instructions for building living organisms. Because of the fact that proteins are constructed in accordance with the genetic instructions encoded in DNAs, errors in RNA synthesis and translation into proteins can cause genetic disorders. Therefore, understanding and recognizing genetic sequences is one step towards the treatment of these genetic disorders. Since the discovery of DNA, there has been a growing interest in the problem of genetic sequence recognition, motivated by its enormous potential to cure a wide range of genetic disorders. 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. This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of recognizing an important class of genetic sequences known as eukaryotic splice junctions. To lower the computational complexity and to increase the generalization capability of the system, we employ a genetic algorithm to select relevant nucleotides that are directly responsible for splice-junction recognition. We carried out experiments on a dataset extracted from the biological literature. This proposed system has achieved an accuracy of 96.68% in classifying splice-junction genetic sequences. The experimental results demonstrate the efficacy of our framework and encourage us to apply the framework on other types of genetic sequences.
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	Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
title_short	Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
title_full	Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
title_fullStr	Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
title_full_unstemmed	Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
title_sort	classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
publisher	Elsevier Ltd.
publishDate	2013
url	http://irep.iium.edu.my/34320/ http://irep.iium.edu.my/34320/ http://irep.iium.edu.my/34320/ http://irep.iium.edu.my/34320/6/Classification_of_eukaryotic_splice-junction_genetic_sequences.pdf
first_indexed	2023-09-18T20:49:29Z
last_indexed	2023-09-18T20:49:29Z
_version_	1777409898110255104

Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution

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