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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.
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BMC Bioinformatics

ISSN: 1471-2105

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