Figure 3.

The search behavior of the segment modeling algorithm using the whole data set. Pairwise plots showing reduced learning error (2) at each iteration of segment merging and model recalculation. The columns for the pairwise plots are k-mers; rows are cell lines. In each plot, the X-axis denotes the number of iterations and the weighted squared prediction error is denoted on the Y-axis. The HillClimbing search algorithm effectively reduced the error between prediction and observation. In fitting the whole data set, as opposed to 10 fold cross validation, the final model predicted methylation susceptibility in the different cell types.

Yang et al. BMC Bioinformatics 2012 13(Suppl 3):S15   doi:10.1186/1471-2105-13-S3-S15