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This article is part of the supplement: The 2007 International Conference on Bioinformatics & Computational Biology (BIOCOMP'07)

Open Access Highly Accessed Research

A comparative study of different machine learning methods on microarray gene expression data

Mehdi Pirooznia1, Jack Y Yang2, Mary Qu Yang3 and Youping Deng1*

Author Affiliations

1 Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, 39406, USA

2 Harvard Medical School, Harvard University, Cambridge, Massachusetts 02140, USA

3 National Human Genome Research Institute, National Institutes of Health (NIH), U.S. Department of Health and Human Services Bethesda, MD 20852, USA

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BMC Genomics 2008, 9(Suppl 1):S13  doi:10.1186/1471-2164-9-S1-S13

Published: 20 March 2008

Abstract

Background

Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results.

Results

In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods. The v-fold cross validation was used to calculate the accuracy of the classifiers. Some of the common clustering methods including K-means, DBC, and EM clustering were applied to the datasets and the efficiency of these methods have been analysed. Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared. In each case these methods were applied to eight different binary (two class) microarray datasets. We evaluated the class prediction efficiency of each gene list in training and test cross-validation using supervised classifiers.

Conclusions

We presented a study in which we compared some of the common used classification, clustering, and feature selection methods. We applied these methods to eight publicly available datasets, and compared how these methods performed in class prediction of test datasets. We reported that the choice of feature selection methods, the number of genes in the gene list, the number of cases (samples) substantially influence classification success. Based on features chosen by these methods, error rates and accuracy of several classification algorithms were obtained. Results revealed the importance of feature selection in accurately classifying new samples and how an integrated feature selection and classification algorithm is performing and is capable of identifying significant genes.