This article is part of the supplement: Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009) .

Research

A voting approach to identify a small number of highly predictive genes using multiple classifiers

Md Rafiul Hassan¹ , M Maruf Hossain¹ , James Bailey^1,2 , Geoff Macintyre^1,2 , Joshua WK Ho^3,4 and Kotagiri Ramamohanarao^1,2

¹  Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia

²  NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Australia

³  School of Information Technologies, The University of Sydney, NSW 2006, Australia

⁴  NICTA, Australian Technology Park, Eveleigh, NSW 2015, Australia

author email corresponding author email

BMC Bioinformatics 2009, 10(Suppl 1):S19doi:10.1186/1471-2105-10-S1-S19

Published:	30 January 2009

Abstract

Background

Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage.

Results

By using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer.

Conclusion

We show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.