Fig. 4

A comparison among the performances of AnnoPRO and three representative methods. The performances were represented using AUC values in predicting the experimentally validated new protein functions that were not included in CAFA4 data, and the performances of AnnoPRO, DeepGOPlus, NetGO3 and PFmulDL were highlighted in light red, light green, orange and light blue, respectively. For GO families in the ‘Head Label Levels’ (LEVEL 2 and LEVEL 3 provided in Additional file 1: Fig. S3), the performance of AnnoPRO was roughly as good as that of the other three methods (1.4 ~ 4.1% improvements in most cases, but 0.1% decline in one single case). For the GO families in the ‘Tail Label Levels’ (LEVEL 4 to LEVEL 10 shown in Additional file 1: Fig. S3), AnnoPRO demonstrated the consistently superior performance among four methods (1.7 ~ 28.2% improvements in all cases). Particularly, 13 (61.9%) out of all 21 improvements were over 5%, and 6 (28.6%) out of 21 improvements were more than 10%. Therefore, AnnoPRO was identified superior in significantly improving the annotation performances of the families in ‘Tail Label Levels’ without sacrificing that of the ‘Head Label Levels’, which was highly expected to make contribution to solving the long-standing ‘long-tail problem’[18] in functional annotation