An expert system to classify microarray gene expression data using gene selection by decision tree
Journal
Expert Systems with Applications
Journal Volume
36
Journal Issue
5
Date Issued
2009-07-01
Author(s)
Abstract
Gene selection can help the analysis of microarray gene expression data. However, it is very difficult to obtain a satisfactory classification result by machine learning techniques because of both the curse-of-dimensionality problem and the over-fitting problem. That is, the dimensions of the features are too large but the samples are too few. In this study, we designed an approach that attempts to avoid these two problems and then used it to select a small set of significant biomarker genes for diagnosis. Finally, we attempted to use these markers for the classification of cancer. This approach was tested the approach on a number of microarray datasets in order to demonstrate that it performs well and is both useful and reliable. © 2008 Elsevier Ltd. All rights reserved.
Subjects
Bioinformatics
Decision tree
Expert system
Machine learning
Microarray gene expression
Type
journal article