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: | , , , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
SERSC
2015
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| Subjects: | |
| 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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| Summary: | 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. |
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