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Bioinformatics Advance Access originally published online on January 10, 2006
Bioinformatics 2006 22(6):755-761; doi:10.1093/bioinformatics/btk036
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

Zuyi Wang 1,2, Yue Wang 3,*, Jianhua Xuan 2, Yibin Dong 3, Marina Bakay 1, Yuanjian Feng 3, Robert Clarke 4 and Eric P. Hoffman 1

1Center for Genetic Medicine, Children's National Medical Center Washington, DC 20010, USA
2Department of Electrical Engineering and Computer Science, The Catholic University of America Washington, DC 20064, USA
3The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University Arlington, VA 22203, USA
4Departments of Oncology, Physiology and Biophysics, Lombardi Comprehensive Cancer Center, Georgetown University Washington, DC 20007, USA

*To whom correspondence should be addressed.

Motivation: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy.

Results: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (Az).

Supplementary information: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm

Contact: yuewang{at}vt.edu

Received on August 23, 2005; revised on November 23, 2005; accepted on December 30, 2005

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