Table 4 Standard performance benchmarks for MutPred Splice based on an unseen test set of 352 variants (238 positive, 114 negative) using the three different iterations (Iter. 1, Iter 2. and Iter. 3) of the four different training sets identified in this study (Table 2 )
From: MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing
Data set | False positive rate (%) | Sensitivity (%) | Specificity (%) | Accuracy (%) | AUC (%) | MCC | |
|---|---|---|---|---|---|---|---|
Disease negative set | Iter. 1 | 7.0 | 53.4 | 93.0 | 73.2 | 75.2 | 0.45 |
Iter. 2 | 7.0 | 52.5 | 93.0 | 72.8 | 75.9 | 0.44 | |
Iter. 3 | 4.4 | 55.0 | 95.6 | 75.3 | 77.1 | 0.49 | |
SNP negative set | Iter. 1 | 36.8 | 73.1 | 63.2 | 68.1 | 76.4 | 0.35 |
Iter. 2 | 36.8 | 72.3 | 63.2 | 67.7 | 76.8 | 0.34 | |
Iter. 3 | 34.2 | 71.0 | 65.8 | 68.4 | 78.3 | 0.35 | |
Mixed negative set | Iter. 1 | 7.9 | 56.3 | 92.1 | 74.2 | 78.8 | 0.46 |
Iter. 2 | 7.9 | 56.7 | 92.1 | 74.4 | 78.6 | 0.46 | |
Iter. 3 | 7.0 | 64.7 | 93.0 | 78.8 | 83.5 | 0.54 | |
Random SNP set | Iter. 1 | 0.0 | 1.3 | 100.0 | 50.6 | 50.6 | 0.06 |
Iter. 2 | 0.9 | 1.7 | 99.1 | 50.4 | 45.2 | 0.03 | |
Iter. 3 | 29.8 | 31.1 | 70.2 | 50.6 | 50.3 | 0.01 | |