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A Discriminative Approach for Unsupervised Clustering of DNA Sequence Motifs

Figure 6

Optimization of α-parameters applied in ED and SSD scores.

Optimization selected α- parameters for best performance according to best hit (red) and class-depth statistics (blue) in the range from 0.05 to 0.95. Different subsets of TF classes such as the 5 largest (dashed lines), classes with at least 20 (solid lines) as well as with at least 10 matrices (dotted lines) were also considered. Optimal alpha values were 0.5 for ED.ave and ED.sqr scores, 0.55 for the ED score, 0.25 for SSD.ave and SSD.sqr scores, as well as 0.3 for the SSD score and are indicated by gray dotted lines.

Figure 6

doi: https://doi.org/10.1371/journal.pcbi.1002958.g006