Classification Of Microarray Datasets Using Random Forest

DNA microarray technology has enabled the capability to monitor the expressions of tens of thousands of genes in a biological sample on a single chip. Medical fields can benefit from microarray data mining as it helps in early detection of genes mutation and diagnosis of disease. A well built model...

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Main Author:	Ng, Ee Ling
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA76.9.D32 Databases
Online Access:	http://eprints.usm.my/51469/1/cd%20tesis%20classification%20of%20microarray%20cut.pdf
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id	my-usm-ep.51469
record_format	uketd_dc
spelling	my-usm-ep.514692022-02-09T07:29:26Z Classification Of Microarray Datasets Using Random Forest 2009-06 Ng, Ee Ling QA76.9.D32 Databases DNA microarray technology has enabled the capability to monitor the expressions of tens of thousands of genes in a biological sample on a single chip. Medical fields can benefit from microarray data mining as it helps in early detection of genes mutation and diagnosis of disease. A well built model can be used to predict unknown disease classes in a test case. Prior to a well built model is to achieve good classification results which rely very much on the classifiers that are being used. However, in most microarray data, the number of genes usually outnumbers the number of samples. 2009-06 Thesis http://eprints.usm.my/51469/ http://eprints.usm.my/51469/1/cd%20tesis%20classification%20of%20microarray%20cut.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA76.9.D32 Databases
spellingShingle	QA76.9.D32 Databases Ng, Ee Ling Classification Of Microarray Datasets Using Random Forest
description	DNA microarray technology has enabled the capability to monitor the expressions of tens of thousands of genes in a biological sample on a single chip. Medical fields can benefit from microarray data mining as it helps in early detection of genes mutation and diagnosis of disease. A well built model can be used to predict unknown disease classes in a test case. Prior to a well built model is to achieve good classification results which rely very much on the classifiers that are being used. However, in most microarray data, the number of genes usually outnumbers the number of samples.
format	Thesis
qualification_level	Master's degree
author	Ng, Ee Ling
author_facet	Ng, Ee Ling
author_sort	Ng, Ee Ling
title	Classification Of Microarray Datasets Using Random Forest
title_short	Classification Of Microarray Datasets Using Random Forest
title_full	Classification Of Microarray Datasets Using Random Forest
title_fullStr	Classification Of Microarray Datasets Using Random Forest
title_full_unstemmed	Classification Of Microarray Datasets Using Random Forest
title_sort	classification of microarray datasets using random forest
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Matematik
publishDate	2009
url	http://eprints.usm.my/51469/1/cd%20tesis%20classification%20of%20microarray%20cut.pdf
_version_	1747822072153767936

Classification Of Microarray Datasets Using Random Forest

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