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Table 2 Evaluation of NetSurfP and other surface accessibility predictors.

From: A generic method for assignment of reliability scores applied to solvent accessibility predictions

Method Exposure Train CB513/CB511 Method
Ahmad ASA - 0.48 ANN
Yuan ASA - 0.52 SVR
Nguyan ASA - 0.66 Two-Stage SVR
Real-SPINE ASA 0.74 0.73 ANN
Real-SPINE RSA - 0.70 ANN
NetSurfP ASA 0.75 0.72 ANN
NetSurfP RSA 0.72 0.70 ANN
  1. Performances are shown for 5 different approaches to predict absolute and relative (RSA) surface accessibility. Methods included in the benchmark are Ahmad: [5], Yuan: [20], Nguyen: [24], Real-SPINE: [22], NetSurfP: This work. Train gives the training performance, and CB513/CB511 gives the evaluation performance on the CB513 data set. Train performance of the Real-SPINE method and evaluation performances for the Ahmad, Yuan, and Nguyen method are taken from the corresponding publications. ANN = Artificial neural networks, SVR = Support vector regression. Pearson's correlation coefficients (PCC) are shown for all methods based on the absolute surface exposure of an amino acid. Also, PCC values are given for relative surface exposure for the two methods NetSurfP and Real-SPINE.