Figure 1

Relative performance of six function classification methods on data sets from the SCOP database that has been filtered by 10%, 20%, 30% and 95% pairwise sequence identity, respectively. We used all folds available (42 fold families in 95% sequence identity level), as opposed to our previous study, where only selected folds in the SCOP database was used (14 fold families in 95% sequence identity level). For each function classification method, the number of SCOP folds is plotted against the minimum prediction accuracy achieved by that method. The FSSA algorithm has the overall best performance in function classification when sequence identity is less than 30%.