title: Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data.
abstract: 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
authors.affiliation: Center for Genetic Medicine, Children's National Medical Center Washington, DC 20010, USA.
authors.forename: Zuyi
authors.initials: Z
authors.lastname: Wang
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Yibin
authors.initials: Y
authors.lastname: Dong
authors.forename: Marina
authors.initials: M
authors.lastname: Bakay
authors.forename: Yuanjian
authors.initials: Y
authors.lastname: Feng
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.forename: Eric P
authors.initials: EP
authors.lastname: Hoffman
mesh_terms: 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
chemical_list: D014408:Biomarkers, Tumor; D005819:Genetic Markers