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Table 2 The accuracy of Deep Belief Network, Support Vector Machines, and Neural Networks in terms of Mean Absolute Error (MAE) based on cross validation of training datasets with 16 features, the average per-target correlation, and loss on stage 1 and stage 2 of CASP11 datasets for all three difference techniques. P-value is calculated for the significance of DBN compared to other two methods

From: DeepQA: improving the estimation of single protein model quality with deep belief networks

  MAE based on cross validation Corr. on stage 1/significance of P-value Loss on stage 1/significance of P-value Corr. on stage 2/significance of P-value Loss on stage 2/significance of P-value
Deep Belief Network 0.08 0.63/- 0.09/- 0.34/- 0.06/-
Support Vector Machine 0.12 0.58/1.97E-01 0.10/6.17E-01 0.32/4.45E-04 0.07/7.41E-01
Neural Network 0.08 0.51/9.74E-04 0.12/8.35E-02 0.25/1.05E-05 0.07/1.19E-01
Mean 0.09 0.57/9.88E-02 0.10/3.50E-01 0.30/2.28E-04 0.07/4.30E-01