Effective gene selection techniques for classification of gene expression data

Recent introduction of microarray technology allows researchers to monitor thousands of gene expression levels in a microarray experiment. Classification of tissue samples into tumor or normal is one of the applications of microarray technology. When classifying tissue samples, gene selection plays...

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Main Author:	Yeo, Lee Chin
Format:	Thesis
Language:	English
Published:	2005
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/4222/1/YeoLeeChinMFSKSM2005.pdf
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spelling	my-utm-ep.42222018-01-16T07:12:35Z Effective gene selection techniques for classification of gene expression data 2005-04 Yeo, Lee Chin QA75 Electronic computers. Computer science Recent introduction of microarray technology allows researchers to monitor thousands of gene expression levels in a microarray experiment. Classification of tissue samples into tumor or normal is one of the applications of microarray technology. When classifying tissue samples, gene selection plays an important role. In this research, some existing gene selection techniques are studied and better gene selection techniques are proposed and developed. The proposed approach is carried out by first grouping genes with similar expression profiles into distinct clusters, calculating the cluster quality, calculating the discriminative score for each gene by using statistical techniques, and then selecting informative genes from these clusters based on the cluster quality and discriminative score. The selected subset of genes is then be used to train the classifiers for constructing rules for future tissue classification problem. Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. The statistical techniques used are Fisher Criterion, Golub Signal-to-Noise, Mann-Whitney Rank Sum Statistic and traditional t-test. Support Vector Machine (SVM) and k-nearest neighbour (knn) are used for the classification purposes. The proposed approach is validated using leave one out cross validation (LOOCV). Receiver operating characteristic (ROC) score is used to analyze the results. Colon data with 2000 genes and 62 tissue samples is used for the testing. Highest ROC score recorded from the experiments achieved 0.95, corresponding to five misclassifications. This should be of significant value for diagnostic purposes as well as for guiding further exploration of the underlying biology. 2005-04 Thesis http://eprints.utm.my/id/eprint/4222/ http://eprints.utm.my/id/eprint/4222/1/YeoLeeChinMFSKSM2005.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information Systems
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Yeo, Lee Chin Effective gene selection techniques for classification of gene expression data
description	Recent introduction of microarray technology allows researchers to monitor thousands of gene expression levels in a microarray experiment. Classification of tissue samples into tumor or normal is one of the applications of microarray technology. When classifying tissue samples, gene selection plays an important role. In this research, some existing gene selection techniques are studied and better gene selection techniques are proposed and developed. The proposed approach is carried out by first grouping genes with similar expression profiles into distinct clusters, calculating the cluster quality, calculating the discriminative score for each gene by using statistical techniques, and then selecting informative genes from these clusters based on the cluster quality and discriminative score. The selected subset of genes is then be used to train the classifiers for constructing rules for future tissue classification problem. Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. The statistical techniques used are Fisher Criterion, Golub Signal-to-Noise, Mann-Whitney Rank Sum Statistic and traditional t-test. Support Vector Machine (SVM) and k-nearest neighbour (knn) are used for the classification purposes. The proposed approach is validated using leave one out cross validation (LOOCV). Receiver operating characteristic (ROC) score is used to analyze the results. Colon data with 2000 genes and 62 tissue samples is used for the testing. Highest ROC score recorded from the experiments achieved 0.95, corresponding to five misclassifications. This should be of significant value for diagnostic purposes as well as for guiding further exploration of the underlying biology.
format	Thesis
qualification_level	Master's degree
author	Yeo, Lee Chin
author_facet	Yeo, Lee Chin
author_sort	Yeo, Lee Chin
title	Effective gene selection techniques for classification of gene expression data
title_short	Effective gene selection techniques for classification of gene expression data
title_full	Effective gene selection techniques for classification of gene expression data
title_fullStr	Effective gene selection techniques for classification of gene expression data
title_full_unstemmed	Effective gene selection techniques for classification of gene expression data
title_sort	effective gene selection techniques for classification of gene expression data
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information Systems
publishDate	2005
url	http://eprints.utm.my/id/eprint/4222/1/YeoLeeChinMFSKSM2005.pdf
_version_	1747814505936584704

Effective gene selection techniques for classification of gene expression data

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