Title

Entropy based sub-dimension evaluation and selection method for DNA microarray data classification

Authors

Yi Wang¹ and Hong Yan^{1, 2}

Affiliation

¹School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2006 Australia ²Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong

Email

kingoneonewy@hotmail.com; * Corresponding author

Article Type

Software

Date

received July 21, 2008; accepted September 13, 2008; published November 03, 2008

Abstract

DNA microarray allows the measurement of expression levels of tens of thousands of genes simultaneously and has many applications in biology and medicine. Microarray data are very noisy and this makes it difficult for data analysis and classification. Sub-dimension based methods can overcome the noise problem by partitioning the conditions into sub-groups, performing classification with each group and integrating the results. However, there can be many sub-dimensional groups, which lead to a high computational complexity. In this paper, we propose an entropy-based method to evaluate and select important sub-dimensions and eliminate unimportant ones. This improves the computational efficiency considerably. We have tested our method on four microarray datasets and two other real-world datasets and the experiment results prove the effectiveness of our method.

DNA microarray; datasets; entropy; sub-dimension; probabilistic neural network

Citation

Wang et al., Bioinformation 3(3): 124-129 (2008)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.