Mining Leukemia Gene Association Structure with DNA Microarray

A major focus of cancer research is to infer informative cancer gene association networks from gene expression data. We introduced the relevance network to find the cancer genes´ association that may represent prognostic factors and potential targets for anticancer therapies. On the base of relevance networks using mutual information, interactions of the informative genes are shown graphically and functional genes are clustered. We used a public leukemia data set of 72 RNA expression samples of 50 genes to construct relevance networks. Several relevance networks were produced. The biological significance of relevance networks is explained. These interactions between the genes reveal the mechanism of leukemia and the correlated genes. The results show that the method can be used to find functional genomic clusters and inferring cancer genes´ association networks, independent of previous biological knowledge