A System to Discover Correlations within a Biological Pathway between the Expression Levels of Genes

Current pathway presentation method to the biologist is static graph. The analysis of differentially expressed genes using microarray gene expression data can help to find factors that affect diseases. However, the differentially expressed genes that are identified may be too large in number and it's difficult for biologist to pinpoint the correlations between genes and crucial points on pathway interactively. In this study, we propose a method that attempts to avoid this problem and allows the discovery of greater biological meaning than the traditional method. We select a gene pair set of interacting genes in a biological pathway and investigate the correlation in expression between the gene pairs under different condition (such as relapsed and non-relapsed breast cancer) using microarray gene expression data. We tested the approach using breast cancer relapsed and non-relapsed datasets in order to demonstrate that our method is both useful and reliable; very stable results were obtained when the same microarray platform was used. We finally use an interface to display correlations within a biological pathway.