Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data

A deoxyribonucleic acid (DNA) microarray has the ability to record huge amount of genetic information simultaneously. Previous researches have shown that this technology can be helpful in the classification of cancers and their treatments outcomes. This has encouraged information technology engineer...

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Main Author:	Abdulwahhab, Ahmed Abbas
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Breast - Cancer - Genetic aspects DNA microarrays Genetic regulation
Online Access:	http://psasir.upm.edu.my/id/eprint/56209/1/FK%202015%203RR.pdf
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spelling	my-upm-ir.562092017-06-30T02:14:15Z Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data 2015-11 Abdulwahhab, Ahmed Abbas A deoxyribonucleic acid (DNA) microarray has the ability to record huge amount of genetic information simultaneously. Previous researches have shown that this technology can be helpful in the classification of cancers and their treatments outcomes. This has encouraged information technology engineers to cooperate in microarray data analysis for enhancing medicine and biology technologies. Typically, cancer-related microarray data are consisted of high dimensional gene expression levels (as features) for a limited number of samples. This characteristic in the structure of microarray data causes the phenomenon known as the curse of dimensionality, which is a particularly problem for standard classification models. It contradicts to the required ratio of samples to genes which should be much greater than 1 and it makes the direct application of machine learning techniques inefficient. Consequently, gene selection techniques have become a crucial element in the classification of microarray data. Based on previous researches in the context of microarray data classification, the results obtained from the classification of breast cancer data have the lowest accuracy among them. Therefore, this study was aimed in improving the classification accuracy of clinical outcomes for breast cancer by gene expression profiling. Filter and wrapper for gene selection are the main techniques in many existing microarray data analysis. Promising results obtained from filter-wrapper techniques have led to the design of a proposed model for this study. A gene selection model that integrates rank-partition and sequential wrapper was designed to find optimal subset of the most informative genes that enhances the predictive power of gene expression profiling. Evaluation of the obtained results for breast cancer data set demonstrates that the proposed integrated model achieved the objective in finding the optimal subset of the most informative genes that has the predictive power of 87% accuracy compared to 83% of the original study and 77% of the shrunken centroid method. Breast - Cancer - Genetic aspects DNA microarrays Genetic regulation 2015-11 Thesis http://psasir.upm.edu.my/id/eprint/56209/ http://psasir.upm.edu.my/id/eprint/56209/1/FK%202015%203RR.pdf application/pdf en public masters Universiti Putra Malaysia Breast - Cancer - Genetic aspects DNA microarrays Genetic regulation
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Breast - Cancer - Genetic aspects DNA microarrays Genetic regulation
spellingShingle	Breast - Cancer - Genetic aspects DNA microarrays Genetic regulation Abdulwahhab, Ahmed Abbas Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data
description	A deoxyribonucleic acid (DNA) microarray has the ability to record huge amount of genetic information simultaneously. Previous researches have shown that this technology can be helpful in the classification of cancers and their treatments outcomes. This has encouraged information technology engineers to cooperate in microarray data analysis for enhancing medicine and biology technologies. Typically, cancer-related microarray data are consisted of high dimensional gene expression levels (as features) for a limited number of samples. This characteristic in the structure of microarray data causes the phenomenon known as the curse of dimensionality, which is a particularly problem for standard classification models. It contradicts to the required ratio of samples to genes which should be much greater than 1 and it makes the direct application of machine learning techniques inefficient. Consequently, gene selection techniques have become a crucial element in the classification of microarray data. Based on previous researches in the context of microarray data classification, the results obtained from the classification of breast cancer data have the lowest accuracy among them. Therefore, this study was aimed in improving the classification accuracy of clinical outcomes for breast cancer by gene expression profiling. Filter and wrapper for gene selection are the main techniques in many existing microarray data analysis. Promising results obtained from filter-wrapper techniques have led to the design of a proposed model for this study. A gene selection model that integrates rank-partition and sequential wrapper was designed to find optimal subset of the most informative genes that enhances the predictive power of gene expression profiling. Evaluation of the obtained results for breast cancer data set demonstrates that the proposed integrated model achieved the objective in finding the optimal subset of the most informative genes that has the predictive power of 87% accuracy compared to 83% of the original study and 77% of the shrunken centroid method.
format	Thesis
qualification_level	Master's degree
author	Abdulwahhab, Ahmed Abbas
author_facet	Abdulwahhab, Ahmed Abbas
author_sort	Abdulwahhab, Ahmed Abbas
title	Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data
title_short	Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data
title_full	Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data
title_fullStr	Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data
title_full_unstemmed	Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data
title_sort	integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data
granting_institution	Universiti Putra Malaysia
publishDate	2015
url	http://psasir.upm.edu.my/id/eprint/56209/1/FK%202015%203RR.pdf
_version_	1747812126264655872

Integration of rank-partition and sequential wrapper techniques for feature selection of breast cancer microarray data

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