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Table 4 Comparative results with SVM classifier and Random Forest

From: Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture

Model Input Metrics
Accuracy Sensitivity Specificity MCC
SVM One hot vector 59.65% 46.69% 61.42% 0.054
Physico-chemical property 57.36% 43.84% 59.22% 0.051
PSSM 55.71% 44.29% 57.84% 0.047
Merged 56.92% 44.34% 58.97% 0.049
Random Forest One hot vector 57.27% 45.01% 58.94% 0.026
Physico-chemical property 56.55% 47.40% 57.80% 0.034
PSSM 54.19% 44.98% 56.32% 0.021
Merged 56.52% 46.36% 58.83% 0.024
Our deep architecture One hot vector 64.15% 64.41% 64.08% 0.189
Physico-chemical property 61.84% 60.97% 61.95% 0.151
PSSM 56.82% 58.73% 56.57% 0.099
Merged 66.43% 66.67% 66.40% 0.221