Fig. 3: Performance over GO terms in different ontologies and EC numbers.
From: Structure-based protein function prediction using graph convolutional networks
Precision-recall curves showing the performance of different methods on (a) MF-GO terms and (c) EC numbers on the test set comprised of PDB chains chosen to have ≤30% sequence identity to the chains in the training set. Coverage of the methods is shown in the legend. Distribution of the Fmax score under 100 bootstrap iterations for the top three best-performing methods applied on (b) MF-GO terms and (c) EC numbers computed on the test PDB chains and grouped by maximum % sequence identity to the training set. e Distribution of protein-centric Fmax score and function-centric AUPR score under 10 bootstrap iterations summarized over all test proteins and GO terms/EC numbers, respectively. f Distribution of AUPR score on MF-GO terms of different levels of specificities under 10 bootstrap iterations. Every figure illustrates the performance of DeepFRI (red) in comparison to sequence-based annotation transfer from protein families, FunFams (blue), the CNN-based method DeepGO (orange), SVM-based method, FFPred (black), and BLAST baseline (gray). Error bars on the bar plots (e and f) represent standard deviation of the mean. In panels b and d, data are represented as boxplots with the center line representing the median, upper and lower edges of the boxes representing the interquartile range, and whiskers representing the data range (0.5 × interquartile range).
