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

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. Gene selection using the RankGene software [3] is shown to significantly improve performance on the testing data. Further, we show that the choice of feature selection criteria can have a significant effect on a...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Thanyaluk Jirapech-Umpai, J. Stuart Aitken
• Keywords: BMCBI 2005, Cell Lines, Et Al, Tumour Types