Gene Expression (Microarray) Data Analysis by Chemometric Methods

Title: Gene Expression (Microarray) Data Analysis by Chemometric Methods

Volume: 3 Issue: 3

Author(s): David X. Zhu, Richard J. Goeke, David L. Baker, James H. Hamburg, David E. Booth and Stephane E. Booth

Affiliation:

Keywords: Leukemia, Classification, Neural networks, Cancer, Microarrays, Genes

Abstract: Three sets of optimal leukemia class predictors (genes) were obtained by three different methods from three different authors from the same set of data. We tested these three optimal sets using back propagation neural networks (which were not used by the original authors) with three fold cross validation and leave one out cross validation. We found that the predictor sets performed less well with the neural networks than with the original methods though not in all cases. We discuss this result and suggest methods to use in order to possibly take advantage of this finding.

Cite this article as:

David X. Zhu , Richard J. Goeke , David L. Baker , James H. Hamburg , David E. Booth and Stephane E. Booth , Gene Expression (Microarray) Data Analysis by Chemometric Methods, Current Analytical Chemistry 2007; 3 (3) . https://dx.doi.org/10.2174/157341107781023802