Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes.

ABSTRACT:

Background

In the clinical context, samples assayed by microarray are often classified by cell line or tumour type and it is of interest to discover a set of genes that can be used as class predictors. The leukemia dataset of Golub et al. 1 and the NCI60 dataset of Ross et al. 2 present multiclass classification problems where three tumour types and nine cell lines respectively must be identified. We apply an evolutionary algorithm to identify the near-optimal set of predictive genes that classify the data. We also examine the initial gene selection step whereby the most informative genes are selected from the genes assayed.

Results

In the absence of feature selection, classification accuracy on the training data is typically good, but not replicated on the testing data. Gen

SUBMITTER: Jirapech-Umpai T 

PROVIDER: S-EPMC1181625 | biostudies-literature | 2005 Jun

REPOSITORIES: biostudies-literature