Comparative study of feature selection method of microarray data for gene classification

Recent advances in biotechnology such as microarray, offer the ability to measure the levels of expression of thousands of genes in parallel. Analysis of microarray data can provide understanding and insight into gene function and regulatory mechanisms. This analysis is crucial to identify and class...

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Main Author:	Ghazali, Nurulhuda
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/11502/6/NurulhudaGhazaliMFSKSM2009.pdf
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id	my-utm-ep.11502
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spelling	my-utm-ep.115022017-09-20T10:00:12Z Comparative study of feature selection method of microarray data for gene classification 2009-10 Ghazali, Nurulhuda QA75 Electronic computers. Computer science RC0254 Neoplasms. Tumors. Oncology (including Cancer) Recent advances in biotechnology such as microarray, offer the ability to measure the levels of expression of thousands of genes in parallel. Analysis of microarray data can provide understanding and insight into gene function and regulatory mechanisms. This analysis is crucial to identify and classify cancer diseases. Recent technology in cancer classification is based on gene expression profile rather than on morphological appearance of the tumor. However, this task is made more difficult due to the noisy nature of microarray data and the overwhelming number of genes. Thus, it is an important issue to select a small subset of genes to represent thousands of genes in microarray data which is referred as informative genes. These informative genes will then be classified according to its appropriate classes. To achieve the best solution to the classification issue, we proposed an approach of minimum Redundancy-Maximum Relevance feature selection method together with Probabilistic Neural Network classifier. The minimum Redundancy- Maximum Relevance feature selection method is used to select the informative genes while the Probabilistic Neural Network classifier acts as the classifier. This approach has been tested on a well-known cancer dataset which is Leukemia. The results achieved shows that the gene selected had given high classification accuracy. This reduction of genes helps take out some burdens from biologist and better classification accuracy can be used widely to detect cancer in early stage. 2009-10 Thesis http://eprints.utm.my/id/eprint/11502/ http://eprints.utm.my/id/eprint/11502/6/NurulhudaGhazaliMFSKSM2009.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science QA75 Electronic computers Computer science Ghazali, Nurulhuda Comparative study of feature selection method of microarray data for gene classification
description	Recent advances in biotechnology such as microarray, offer the ability to measure the levels of expression of thousands of genes in parallel. Analysis of microarray data can provide understanding and insight into gene function and regulatory mechanisms. This analysis is crucial to identify and classify cancer diseases. Recent technology in cancer classification is based on gene expression profile rather than on morphological appearance of the tumor. However, this task is made more difficult due to the noisy nature of microarray data and the overwhelming number of genes. Thus, it is an important issue to select a small subset of genes to represent thousands of genes in microarray data which is referred as informative genes. These informative genes will then be classified according to its appropriate classes. To achieve the best solution to the classification issue, we proposed an approach of minimum Redundancy-Maximum Relevance feature selection method together with Probabilistic Neural Network classifier. The minimum Redundancy- Maximum Relevance feature selection method is used to select the informative genes while the Probabilistic Neural Network classifier acts as the classifier. This approach has been tested on a well-known cancer dataset which is Leukemia. The results achieved shows that the gene selected had given high classification accuracy. This reduction of genes helps take out some burdens from biologist and better classification accuracy can be used widely to detect cancer in early stage.
format	Thesis
qualification_level	Master's degree
author	Ghazali, Nurulhuda
author_facet	Ghazali, Nurulhuda
author_sort	Ghazali, Nurulhuda
title	Comparative study of feature selection method of microarray data for gene classification
title_short	Comparative study of feature selection method of microarray data for gene classification
title_full	Comparative study of feature selection method of microarray data for gene classification
title_fullStr	Comparative study of feature selection method of microarray data for gene classification
title_full_unstemmed	Comparative study of feature selection method of microarray data for gene classification
title_sort	comparative study of feature selection method of microarray data for gene classification
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information System
publishDate	2009
url	http://eprints.utm.my/id/eprint/11502/6/NurulhudaGhazaliMFSKSM2009.pdf
_version_	1747814864783409152

Comparative study of feature selection method of microarray data for gene classification

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