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...
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Format: | Article |
Language: | English |
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AIRCC Publishing Corporation
2014
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Online Access: | http://irep.iium.edu.my/37774/1/3214ijci01.pdf |
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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 |
collection | IIUM |
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. |
first_indexed | 2024-03-05T23:28:25Z |
format | Article |
id | oai:generic.eprints.org:37774 |
institution | International Islamic University Malaysia |
language | English |
last_indexed | 2024-03-05T23:28:25Z |
publishDate | 2014 |
publisher | AIRCC Publishing Corporation |
record_format | dspace |
spelling | oai:generic.eprints.org: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 |
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 |
title | 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_short | 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 |
topic | Q Science (General) |
url | http://irep.iium.edu.my/37774/1/3214ijci01.pdf |
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