Correlation Coefficient networks: testing the 'boosting' ability of the algorithm. The same experiment as in Figure 6 was carried out on the correlation coefficient networks. In each case the network connectivity is identical but the expression change vector, used as input to the algorithm, is randomly chosen to be one experiment from the stress data set. The x-axis represents the top 200 genes when ranked using expression change information. We calculate a 'boosting' measure to quantify how much we increase the relative rank of each gene after it has been altered. In this case, each gene was changed to have expression change 1. The large values for B (max = 1) indicate that the algorithm achieves a high level of 'boosting' due to the connections that the altered genes have to highly changed genes. The blue line represents the original expression ranked position, the red circles show the original GeneRanked position and the blue asterisks show the modified GeneRanked position.
Morrison et al. BMC Bioinformatics 2005 6:233 doi:10.1186/1471-2105-6-233