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Bioinformatics Advance Access published online on January 10, 2006
Bioinformatics, 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
Received August 23, 2005
Revised November 23, 2005
Accepted December 30, 2005

Article

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

Zuyi Wang 1, Yue Wang 2 *, Jianhua Xuan 3, Yibin Dong 2, Marina Bakay 4, Yuanjian Feng 2, Robert Clarke 5, and Eric P. Hoffman 4

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

* To whom correspondence should be addressed.
Yue Wang, E-mail: yuewang{at}vt.edu


  Abstract

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 due 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 cure of dimensionality in large microarray data sets. 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).

Associate Editor: Martin Bishop
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