Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

Zuyi Wang, Yue Wang, Jianhua Xuan, Yibin Dong, Marina Bakay, Yuanjian Feng, Robert Clarke, Eric P. Hoffman

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

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 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).

Original languageEnglish (US)
Pages (from-to)755-761
Number of pages7
JournalBioinformatics
Volume22
Issue number6
DOIs
StatePublished - Mar 15 2006
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported in part by the National Institutes of Health grants under CA109872, CA096483 and EB000830, and DOD/CDMRP grant under BC030280. Z.W. was also supported by the Crystal Ball of Virginia Beach VAs and the Muscular Dystrophy Association.

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