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

doi:10.1186/1471-2105-10-S1-S19

BMC Bioinformatics

Volume 10
Suppl 1

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Hassan MR
Hossain MM
Bailey J
Macintyre G
Ho JW
Ramamohanarao K

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Bailey J
Macintyre G
Ho JW
Ramamohanarao K
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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 Bailey¹^,2 , Geoff Macintyre¹^,2 , Joshua WK Ho³^,4 and Kotagiri Ramamohanarao¹^,2

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

⁴NICTA, 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

Additional 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.