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Table 1 Performance of basic DBN and NN models and their combinations tested on SD576.

From: A dynamic Bayesian network approach to protein secondary structure prediction

Model Q3 (%) SOV (%) C H C E C C
DBNlinear+NC 75.1 74.0 0.69 0.60 0.55
DBNlinear+CN 74.6 73.3 0.68 0.61 0.53
DBNlinear 77.0 75.8 0.72 0.64 0.58
DBNsigmoid+NC 75.8 74.5 0.72 0.60 0.56
DBNsigmoid+CN 74.6 73.3 0.69 0.61 0.54
DBNsigmoid 77.4 75.9 0.74 0.64 0.59
DBNfinal 78.2 76.8 0.74 0.65 0.60
NNlinear 77.6 73.2 0.72 0.64 0.60
NNsigmoid 77.1 71.0 0.72 0.63 0.59
NNfinal 77.8 73.3 0.73 0.64 0.60
DBNN 80.0 78.1 0.77 0.68 0.63
  1. All the eleven models listed in the table are described in Methods. The average results of seven-fold cross-validation are shown.