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
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* Corresponding authors: Md Rafiul Hassan mrhassan@csse.unimelb.edu.au - M Maruf Hossain hossain@csse.unimelb.edu.au
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
BMC Bioinformatics 2009, 10(Suppl 1):S19 doi:10.1186/1471-2105-10-S1-S19
Published: 30 January 2009Additional files
Additional file 1:
This file contains the rank gene list used in each fold of 5-fold CV, and performance of each fold using the selected genes for different classifier.
Format: PDF Size: 103KB Download file
This file can be viewed with: Adobe Acrobat Reader
Additional file 2:
This file contains the result of gene set enrichment analysis (GSEA).
Format: ZIP Size: 1.3MB Download file