Fig. 4
From: MAGPIE: accurate pathogenic prediction for multiple variant types using machine learning approach
MAGPIE outperforms other models in orthogonal validation set and ACMG-guided dataset. A The pie chart showed the proportion of pathogenic and benign variants in the orthogonal validation set and the bar plot illustrated the percentages of multi-type variants in the dataset. B The receiver operating characteristic curve of MAGPIE and 14 other predicted tools in the orthogonal validation set. C The precision-recall curve of MAGPIE and 14 other predicted tools in the orthogonal validation set were illustrated. D Missing rate comparison of MAGPIE and 14 other predicted tools in the orthogonal validation set. The higher missing rate represented that the prediction tools cannot predict pathogenic scores on the larger number of candidate variants. E AUC comparison of MAGPIE and 14 other predicted tools in the SwissProtRare which only included variants with AF < 0.01. F AUBPRC comparison of MAGPIE and 14 other predicted tools in the SwissProtRare which only included variants with AF < 0.01. G The pie chart showed the proportion of pathogenic and benign variants in the ACMG-guided dataset, and the bar plot illustrated the percentages of multi-type variants in the dataset. H Performance comparison of MAGPIE and 14 other predicted tools in the ACMG-guided dataset. I The precision-recall curve of MAGPIE and 14 other predicted tools in the ACMG-guided dataset were illustrated