Abstract:

To solve the microarray data problem that the data has small sample size and each dimension of the sample is high, therefor there is a lot of interfering information with redundant information in the data. The multi-stage algorithm of informative gene set selection is discussed in this paper. First, the difference of single gene in different condition is considered, genes with more differences in special condition are selected as candidate gene set. Then, the genes with less correlation are rejected. Finally, the better gene sets based on the global classification performance of any set are selected. The experiment result shows that the algorithm is feasible and effective for informative gene set selection. The feature set of genes in both fitness of classification (classification ca
pability metrics) and classification accuracy is more accurate, and more efficient than the raw data.

Key words: microarray expression profiles, informative gene, correlation, genetic algorithm