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A Review of Cancer Classification Software for Gene Expression Data

Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of...

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Main Authors: Tan, Ching Siang, Ting, Wai Soon, Shahreen, Kasim, Mohd Saberi, Mohamad, Chan, Weng Howe, Safaai, Deris, Zalmiyah, Zakaria, Zuraini, Ali Shah, Zuwairie, Ibrahim
Format: Article
Language:English
English
Published: SERSC 2015
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/11602/1/A%20Review%20of%20Cancer%20Classification%20Software%20for%20Gene%20Expression%20Data.pdf
http://umpir.ump.edu.my/id/eprint/11602/7/A%20Review%20of%20Cancer%20Classification%20Software%20for%20Gene%20Expression%20Data.pdf
http://umpir.ump.edu.my/id/eprint/11602/
http://dx.doi.org/10.14257/ijbsbt.2015.7.4.10
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spelling my.ump.umpir.116022018-02-08T02:58:24Z http://umpir.ump.edu.my/id/eprint/11602/ A Review of Cancer Classification Software for Gene Expression Data Tan, Ching Siang Ting, Wai Soon Shahreen, Kasim Mohd Saberi, Mohamad Chan, Weng Howe Safaai, Deris Zalmiyah, Zakaria Zuraini, Ali Shah Zuwairie, Ibrahim TK Electrical engineering. Electronics Nuclear engineering Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest. SERSC 2015 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/11602/1/A%20Review%20of%20Cancer%20Classification%20Software%20for%20Gene%20Expression%20Data.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/11602/7/A%20Review%20of%20Cancer%20Classification%20Software%20for%20Gene%20Expression%20Data.pdf Tan, Ching Siang and Ting, Wai Soon and Shahreen, Kasim and Mohd Saberi, Mohamad and Chan, Weng Howe and Safaai, Deris and Zalmiyah, Zakaria and Zuraini, Ali Shah and Zuwairie, Ibrahim (2015) A Review of Cancer Classification Software for Gene Expression Data. International Journal of Bio-Science and Bio-Technology (IJBSBT), 7 (4). pp. 89-108. ISSN 2233-7849 http://dx.doi.org/10.14257/ijbsbt.2015.7.4.10 DOI: 10.14257/ijbsbt.2015.7.4.10
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Tan, Ching Siang
Ting, Wai Soon
Shahreen, Kasim
Mohd Saberi, Mohamad
Chan, Weng Howe
Safaai, Deris
Zalmiyah, Zakaria
Zuraini, Ali Shah
Zuwairie, Ibrahim
A Review of Cancer Classification Software for Gene Expression Data
description Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest.
format Article
author Tan, Ching Siang
Ting, Wai Soon
Shahreen, Kasim
Mohd Saberi, Mohamad
Chan, Weng Howe
Safaai, Deris
Zalmiyah, Zakaria
Zuraini, Ali Shah
Zuwairie, Ibrahim
author_facet Tan, Ching Siang
Ting, Wai Soon
Shahreen, Kasim
Mohd Saberi, Mohamad
Chan, Weng Howe
Safaai, Deris
Zalmiyah, Zakaria
Zuraini, Ali Shah
Zuwairie, Ibrahim
author_sort Tan, Ching Siang
title A Review of Cancer Classification Software for Gene Expression Data
title_short A Review of Cancer Classification Software for Gene Expression Data
title_full A Review of Cancer Classification Software for Gene Expression Data
title_fullStr A Review of Cancer Classification Software for Gene Expression Data
title_full_unstemmed A Review of Cancer Classification Software for Gene Expression Data
title_sort review of cancer classification software for gene expression data
publisher SERSC
publishDate 2015
url http://umpir.ump.edu.my/id/eprint/11602/1/A%20Review%20of%20Cancer%20Classification%20Software%20for%20Gene%20Expression%20Data.pdf
http://umpir.ump.edu.my/id/eprint/11602/7/A%20Review%20of%20Cancer%20Classification%20Software%20for%20Gene%20Expression%20Data.pdf
http://umpir.ump.edu.my/id/eprint/11602/
http://dx.doi.org/10.14257/ijbsbt.2015.7.4.10
_version_ 1643666711624286208
score 13.211869

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