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Influence of Prior Information on the Reconstruction of the Yeast Cell Cycle from Microarray Data

In this paper we suggest a method to reconstruct the gene interaction network of the cell cycle of S.cerevisiae from microarray data. We study a small subnetwork comprising 20 genes and estimate iteratively partial correlations between expression profiles of the corresponding genes. Starting from a fully connected network an edge is deleted if the estimated partial correlation coefficient is not statistically significant from zero. To find an appropriate significance level for the statistical test we use prior information from the literature and calculate a so called efficient significance level used for the hypothesis tests. That means, our method is supervised and by this adaptive to various noise levels in the data.