Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators

Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very l...

Full description

Bibliographic Details
Main Authors:	Shoon , Lei Win, Htike@Muhammad Yusof, Zaw Zaw, Yusof, Faridah, Ibrahim Ali , Noorbatcha
Format:	Article
Language:	English
Published:	AIRCC Publishing Corporation 2014
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/37774/ http://irep.iium.edu.my/37774/ http://irep.iium.edu.my/37774/ http://irep.iium.edu.my/37774/1/3214ijci01.pdf

id	iium-37774
recordtype	eprints
spelling	iium-377742018-06-19T08:27:31Z http://irep.iium.edu.my/37774/ Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators Shoon , Lei Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Ibrahim Ali , Noorbatcha Q Science (General) Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Therefore, it is of paramount importance to prevent and detect cancer early. Nonetheless, conventional methods of detecting and diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the late stages of cancer. The microarray gene expression technology is a promising technology that can detect cancerous cells in early stages of cancer by analyzing gene expression of tissue samples. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. 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 cancer from DNA microarray gene expression data. To lower the computational complexity and to increase the generalization capability of the system, we employ an entropy-based gene selection approach to select relevant gene that are directly responsible for cancer discrimination. This proposed system has achieved an average accuracy of 98.94% in recognizing and classifying cancer over 11 benchmark cancer datasets. The experimental results demonstrate the efficacy of our framework. AIRCC Publishing Corporation 2014-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37774/1/3214ijci01.pdf Shoon , Lei Win and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah and Ibrahim Ali , Noorbatcha (2014) Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators. International Journal on Cybernetics & Informatics (IJCI) , 3 (2). pp. 1-10. ISSN 2277-548X (O) 2320-8430 (P) http://airccse.org/journal/ijci/Current2014.html 10.5121/ijci.2014.3201
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) Shoon , Lei Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Ibrahim Ali , Noorbatcha Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators
description	Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late. Therefore, it is of paramount importance to prevent and detect cancer early. Nonetheless, conventional methods of detecting and diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the late stages of cancer. The microarray gene expression technology is a promising technology that can detect cancerous cells in early stages of cancer by analyzing gene expression of tissue samples. The microarray technology allows researchers to examine the expression of thousands of genes simultaneously. 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 cancer from DNA microarray gene expression data. To lower the computational complexity and to increase the generalization capability of the system, we employ an entropy-based gene selection approach to select relevant gene that are directly responsible for cancer discrimination. This proposed system has achieved an average accuracy of 98.94% in recognizing and classifying cancer over 11 benchmark cancer datasets. The experimental results demonstrate the efficacy of our framework.
format	Article
author	Shoon , Lei Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Ibrahim Ali , Noorbatcha
author_facet	Shoon , Lei Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Ibrahim Ali , Noorbatcha
author_sort	Shoon , Lei Win
title	Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators
title_short	Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators
title_full	Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators
title_fullStr	Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators
title_full_unstemmed	Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators
title_sort	cancer recognition from dna microarray gene expression data using averaged one-dependence estimators
publisher	AIRCC Publishing Corporation
publishDate	2014
url	http://irep.iium.edu.my/37774/ http://irep.iium.edu.my/37774/ http://irep.iium.edu.my/37774/ http://irep.iium.edu.my/37774/1/3214ijci01.pdf
first_indexed	2023-09-18T20:54:10Z
last_indexed	2023-09-18T20:54:10Z
_version_	1777410192312369152

Cancer recognition from DNA microarray gene expression data using averaged one-dependence estimators

Similar Items