Fig. 3: Comparisons among different methods across GO terms in various ontologies and EC numbers.
From: Accurate prediction of protein function using statistics-informed graph networks
a Precision-recall curves illustrate the performance of different methods in predicting EC numbers for proteins. b Protein-centric Fmax scores and function-centric AUPR scores are computed across all test proteins to predict EC numbers, where the scores are presented as mean values with standard deviations of 10 bootstrap iterations. c Evaluation of robustness in predicting EC numbers as sequence identity increases, where the Fmax scores of each method at different sequence identities are depicted as boxplots of 50 bootstrap iterations, with the median values at the center and the interquartile range shown by the upper and lower edges of the boxes. d Precision-recall performance across GO terms in different ontologies. e Left, violin plots showing AUPR with the median values at the center of the distribution of 10 bootstrap iterations, and right, Fmax scores for the different methods in predicting CC, BP, and MF. f Computed Matthews correlation coefficient between predicted scores and ground-truth values for both EC numbers and GO terms using different methods. Source data are provided as a Source Data file.
