Random forest for gene selection and microarray data classification

• Published in: Bioinformation Vol. 7; no. 3; pp. 142 - 146
• Main Authors: Moorthy, Kohbalan, Mohamad, Mohd Saberi
• Format: Journal Article
• Language: English
• Published: Singapore Biomedical Informatics 01.01.2011
• Subjects: Hypothesis; gene expression data; gene selection; Random forest; cancer classification; classification; microarray data
• ISSN: 0973-8894; 0973-2063; 0973-2063
• DOI: 10.6026/97320630007142

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.