Table 4 Comparative performance of DBNN against other popular methods tested on CB513.
From: A dynamic Bayesian network approach to protein secondary structure prediction
Method | Q3 (%) | SOV (%) | C H | C E | C C |
|---|---|---|---|---|---|
SVM | 73.5 | -- | 0.65 | 0.53 | 0.54 |
PMSVM | 75.2 | -- | 0.71 | 0.61 | 0.61 |
SVMpsi | 76.6 | 73.5 | 0.68 | 0.60 | 0.56 |
JNET | 76.9 | -- | -- | -- | -- |
YASSPP | 77.8 | 75.1 | 0.58 | 0.64 | 0.71 |
†SPINE | 76.8 | -- | -- | -- | -- |
DBNN/ErrSig | 78.1/0.41 | 74.0/0.62 | 0.74/0.01 | 0.64/0.01 | 0.60/0.01 |
†DBNN/ErrSig | 78.0/0.40 | 74.0/0.62 | 0.74/0.01 | 0.64/0.01 | 0.60/0.01 |