BMC Bioinformatics BioMed Central Research Open Access A voting approach to identify a small number of highly predictive genes using multiple classifiers Md Rafiul Hassan*1, M Maruf Hossain*1, James Bailey1,2, Geoff Macintyre1,2, Joshua WK Ho3,4 and Kotagiri Ramamohanarao1,2 Address: 1Department of Computer Science and Software Engineering, The University of Melbourne, Victoria 3010, Australia, 2NICTA Victoria Laboratory, The University of Melbourne, Victoria 3010, Australia, 3School of Information Technologies, The University of Sydney, NSW 2006, Australia and 4NICTA, Australian Technology Park, Eveleigh, NSW 2015, Australia Email: Md Rafiul Hassan* -
[email protected]; M Maruf Hossain* -
[email protected]; James Bailey -
[email protected]; Geoff Macintyre -
[email protected]; Joshua WK Ho -
[email protected]; Kotagiri Ramamohanarao -
[email protected] * Corresponding authors from The Seventh Asia Pacific Bioinformatics Conference (APBC 2009) Beijing, China. 13–16 January 2009 Published: 30 January 2009 <supplement> <title> <p>Selected papers from the Seventh Asia-Pacific Bioinformatics Conference (APBC 2009)</p> </title> <editor>Michael Q Zhang, Michael S Waterman and Xuegong Zhang</editor> <note>Research</note> </supplement> BMC Bioinformatics 2009, 10(Suppl 1):S19 doi:10.1186/1471-2105-10-S1-S19 This article is available from: http://www.biomedcentral.com/1471-2105/10/S1/S19 © 2009 Hassan et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.