A voting approach to identify a small number of highly predictive genes using multiple classifiers
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.Conclusions: 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.
Rafiul Hassan M.Maruf Hossain James Bailey Geoff Macintyre Joshua W.K.Ho Kotagiri Ramamohanarao
Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, School of Information Technologies, The University of Sydney, NSW 2006, Australia NICTA, Australian
国际会议
The 7th Asia-Pacific Bioinformatics Conference(第七届亚太生物信息学大会)
北京
英文
194-205
2009-01-01（万方平台首次上网日期，不代表论文的发表时间）