Beyond Bioinformatics

Title

On sparse Fisher discriminant method for microarray data analysis

Authors

Eric S. Fung¹ and Michael K. Ng^1, *

Affiliation

¹Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, HongKong

Email

mng@math.hkbu.edu.hk; * Corresponding author

Article Type

Hypothesis

Date

received December 14, 2007; accepted December 28, 2007; published online December 30, 2007

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calculate a weight for each gene and use the weight values as an indicator to identify the subsets of relevant genes that categorize patient and normal samples. A l₂ - l₁ norm minimization method is implemented to the discriminant process to automatically compute the weights of all genes in the samples. The experiments on two microarray data sets have shown that the new algorithm can generate classification results as good as other classification methods, and effectively determine relevant genes for classification purpose. In this study, we demonstrate the gene selection’s ability and the computational effectiveness of the proposed algorithm. Experimental results are given to illustrate the usefulness of the proposed model.

microarray; Fisher discriminant method; data; genes; algorithm

Citation

Fung & Ng, Bioinformation 2(5): 222-229 (2007)

Edited by

T. W. Tan & S. Ranganathan

ISSN

0973-2063

Publisher

Biomedical Informatics Publishing Group

Copyright

Publisher

Copyright Transfer Agreement

The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.

License

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.