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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
  1. The description of DBNN can be found in Methods. Entries marked with "--" mean that the data could not be obtained from literatures. JNET has been trained and tested on the CB480 dataset (a reduced version of CB513), while all other methods have been trained and tested on the CB513 dataset. Methods marked with "†" have been evaluated using ten-fold cross-validation, while others have been evaluated using seven-fold cross-validation.