Paper Title
Gene Interaction Networks Based On Nonparametric Correlation Coefficients In Saccharomyces Cerevisiae Data

It is an essential and complicated task to extract two-way interactions from microarray data. Several methods of data mining have been widely created such as clustering and gene set analyses to link genes that show similar expression patterns. However, these methods generally fail to unveil gene-gene interactions in the same cluster. Therefore, in the present study, we applied several nonparametric correlation coefficient methods to transform the linear rank statistics into distance metrics on a Saccharomyces cerevisiae data set. These correlation-based networks were then compared with Pearson correlation method. The reliability and advantages of our proposedis confirmed by twowebsites. The results of biological interactions and characteristics showed that the proposed nonparametriccorrelation coefficient methods have a strong capability to identify interaction genes. Moreover, proposed techniques could accurately detect the key genes and functional interactions in comparison to generally used Pearson correlation coefficient. Keywords - Gene network, Two-way gene interaction, Non parametric, Saccharomyces cerevisiae.