Gene expression mining for predicting survivability of patients in early stages of lung cancer

After numerous breakthroughs in medicine, microbiology, and pathology in the past century, lung cancer still remains as a leading cause of cancer-related death even in the developed countries. Lung cancer accounts roughly for 30% of all cancer-related deaths in the world. Diagnosis and treatments ar...

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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/37772/ http://irep.iium.edu.my/37772/ http://irep.iium.edu.my/37772/1/4214ijbb01.pdf

id	iium-37772
recordtype	eprints
spelling	iium-377722018-06-12T06:51:00Z http://irep.iium.edu.my/37772/ Gene expression mining for predicting survivability of patients in early stages of lung cancer Shoon , Lei Win Htike@Muhammad Yusof, Zaw Zaw Yusof, Faridah Ibrahim Ali , Noorbatcha Q Science (General) After numerous breakthroughs in medicine, microbiology, and pathology in the past century, lung cancer still remains as a leading cause of cancer-related death even in the developed countries. Lung cancer accounts roughly for 30% of all cancer-related deaths in the world. Diagnosis and treatments are still based on traditional histopathology. It is of paramount importance to predict the survivability of patients in early stages of lung cancer so that specific treatments can be sought. Nonetheless, histopathology has been shown by previous studies to be inadequate in predicting lung cancer development and clinical outcome. 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 predicting whether a patient in early stages of lung cancer will survive by mining DNA microarray gene expression data. To lower the computational complexity, we employ an entropy-based gene selection approach to select relevant genes that are directly responsible for lung cancer survivability prognosis. The proposed system has achieved an average accuracy of 92.31% in predicting lung cancer survivability over 2 independent datasets. The experimental results provide confirmation that gene expression mining can be used to predict survivability of patients in early stages of lung cancer. AIRCC Publishing Corporation 2014-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37772/1/4214ijbb01.pdf Shoon , Lei Win and Htike@Muhammad Yusof, Zaw Zaw and Yusof, Faridah and Ibrahim Ali , Noorbatcha (2014) Gene expression mining for predicting survivability of patients in early stages of lung cancer. International Journal on Bioinformatics & Biosciences (IJBB), 4 (2). pp. 1-9. ISSN 1839-9614 http://airccse.org/journal/IJBB/current2014.html
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 Gene expression mining for predicting survivability of patients in early stages of lung cancer
description	After numerous breakthroughs in medicine, microbiology, and pathology in the past century, lung cancer still remains as a leading cause of cancer-related death even in the developed countries. Lung cancer accounts roughly for 30% of all cancer-related deaths in the world. Diagnosis and treatments are still based on traditional histopathology. It is of paramount importance to predict the survivability of patients in early stages of lung cancer so that specific treatments can be sought. Nonetheless, histopathology has been shown by previous studies to be inadequate in predicting lung cancer development and clinical outcome. 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 predicting whether a patient in early stages of lung cancer will survive by mining DNA microarray gene expression data. To lower the computational complexity, we employ an entropy-based gene selection approach to select relevant genes that are directly responsible for lung cancer survivability prognosis. The proposed system has achieved an average accuracy of 92.31% in predicting lung cancer survivability over 2 independent datasets. The experimental results provide confirmation that gene expression mining can be used to predict survivability of patients in early stages of lung cancer.
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	Gene expression mining for predicting survivability of patients in early stages of lung cancer
title_short	Gene expression mining for predicting survivability of patients in early stages of lung cancer
title_full	Gene expression mining for predicting survivability of patients in early stages of lung cancer
title_fullStr	Gene expression mining for predicting survivability of patients in early stages of lung cancer
title_full_unstemmed	Gene expression mining for predicting survivability of patients in early stages of lung cancer
title_sort	gene expression mining for predicting survivability of patients in early stages of lung cancer
publisher	AIRCC Publishing Corporation
publishDate	2014
url	http://irep.iium.edu.my/37772/ http://irep.iium.edu.my/37772/ http://irep.iium.edu.my/37772/1/4214ijbb01.pdf
first_indexed	2023-09-18T20:54:09Z
last_indexed	2023-09-18T20:54:09Z
_version_	1777410192015622144

Gene expression mining for predicting survivability of patients in early stages of lung cancer

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