Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.
BACKGROUND
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 (A(z)).


BACKGROUND
The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm
Wang
Zuyi
Z
Center for Genetic Medicine, Children's National Medical Center Washington, DC 20010, USA.
Wang
Yue
Y
Xuan
Jianhua
J
Dong
Yibin
Y
Bakay
Marina
M
Feng
Yuanjian
Y
Clarke
Robert
R
Hoffman
Eric P
EP
D014408:Biomarkers, Tumor / Q000032:analysis*; D002874:Chromosome Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor; D005819:Genetic Markers