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. 2003 May;12(5):923–929. doi: 10.1110/ps.0241703

Table 2.

Performance of SNNS and Weka classifiers using multiple alignment and secondary structure information

Multiple alignment Multiple alignment and secondary structure
Weka classifiers Weka classifiers
SNNS (first network) Logistic regression Naive Bayes J48 classifier SNNS (second network) Logistic regression Naive Bayes J48 classifier
Qtotal 76.6 ± 1.8 62.7 ± 1.8 59.0 ± 1.9 92.5 ± 0.2 74.0 ± 1.8 62.6 ± 1.8 57.4 ± 0.9 92.6 ± 0.2
(72.0 ± 2.0) (62.8 ± 1.8) (57.3 ± 1.3) (92.3 ± 0.4)
Qpred 5.1 ± 0.7 5.5 ± 0.7 5.1 ± 0.4 5.0 ± 1.1 6.3 ± 0.7 5.6 ± 0.7 5.0 ± 0.4 5.0 ± 1.2
(6.0 ± 0.7) (5.4 ± 0.7) (4.8 ± 1.0) (5.1 ± 1.3)
Qobs 58.6 ± 2.3 63.9 ± 3.0 65.3 ± 1.8 7.2 ± 0.9 83.2 ± 2.8 65.1 ± 2.9 65.4 ± 1.8 7.2 ± 0.9
(80.0 ± 2.4) (65.1 ± 2.0) (65.4 ± 2.0) (7.4 ± 0.8)
MCC 0.12 ± 0.01 0.10 ± 0.01 0.09 ± 0.01 0.02 ± 0.01 0.17 ± 0.01 0.12 ± 0.01 0.11 ± 0.01 0.03 ± 0.01
(0.16 ± 0.01) (0.12 ± 0.01) (0.11 ± 0.01) (0.03 ± 0.01)

Values in parentheses correspond to the prediction results obtained by excluding the proteins that were used to develop the PSIPRED method.