Mutual Information based Minimum Spanning Trees Model for Selecting Discriminative Genes

Recent studies have shown that gene selection is a crucial technology in microarray data analysis as a result of its large number of genes and relatively small number of samples. Filter methods are fast convergent algorithms with low time complexity. However, filter methods neglect correlation among genes. Other methods for gene selection also have disadvantages. For example, the measurement used to calculate the correlation in other methods can not effectively reflect function similarity among genes, the time complexity will be high based on the whole gene set. Therefore, we propose a novel selection model called mutual information based minimum spanning trees (MIMST) which considers both gene interaction and complementary genes. In this new model, we first use filter methods to remove non-relevant genes, and then compute the interdependence of top-ranked genes. Finally, we construct MST to remove the redundant genes. The experiment results show that MIMST can find the smallest siMIMSTgnificant genes subset with higher classification accuracy compared with other methods.