SELECTING GENES WITH DISSIMILAR DISCRIMINATION STRENGTH FOR SAMPLE CLASS PREDICTION

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SELECTING GENES WITH DISSIMILAR DISCRIMINATION STRENGTH FOR SAMPLE CLASS PREDICTION

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by Zhipeng Cai , Y Goebel , Mohammad R. Salavatipour , Yi Shi , Lizhe Xu , Guohui Lin

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@MISC{Cai_selectinggenes,
    author = {Zhipeng Cai and Y Goebel and Mohammad R. Salavatipour and Yi Shi and Lizhe Xu and Guohui Lin},
    title = {SELECTING GENES WITH DISSIMILAR DISCRIMINATION STRENGTH FOR SAMPLE CLASS PREDICTION},
    year = {}
}

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Abstract

One of the main applications of microarray technology is to determine the gene expression profiles of diseases and disease treatments. This is typically done by selecting a small number of genes from amongst thousands to tens of thousands, whose expression values are collectively used as classification profiles. This gene selection process is notoriously challenging because microarray data normally contains only a very small number of samples, but range over thousands to tens of thousands of genes. Most existing gene selection methods carefully define a function to score the differential levels of gene expression under a variety of conditions, in order to identify top-ranked genes. Such single gene scoring methods suffer because some selected genes have very similar expression patterns so using them all in classification is largely redundant. Furthermore, these selected genes can prevent the consideration of other individually-less but collectively-more differentially expressed genes. We propose to cluster genes in terms of their class discrimination strength and to limit the number of selected genes per cluster. By combining this idea with several existing single gene scoring methods, we show by experiments on two cancer microarray datasets that our methods identify gene subsets which collectively have significantly higher classification accuracies.

Keyphrases

single gene small number top-ranked gene gene expression disease treatment main application microarray data microarray technology gene subset classification profile cancer microarray gene expression profile existing gene selection method expression value classification accuracy gene selection process differential level method suffer class discrimination strength amongst thousand similar expression pattern

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