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Table 7 Summary of results for the regression problem

From: Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Method RMSE r Precision Recall F1 score MCC
SVM 1.34 ± 0.05 0.54 ± 0.03 0.62 ± 0.05 0.44 ± 0.05 0.52 ± 0.03 0.41 ± 0.04
GP 1.36 0.52 0.58 0.47 0.52 0.39
Robetta 1.52 0.47 0.52 0.47 0.49 0.35
LLSF 1.36 0.51 0.54 0.46 0.50 0.36
LLSF(a) 1.45 0.44 0.54 0.42 0.47 0.34
LLSF(b) 1.43 0.47 0.55 0.43 0.48 0.35
  1. SVM: Support Vector Machine, GP: Gaussian Processes, LLSF: Linear Least Squares Fit, LLSF(a): Linear Least Squares Fit with inter-molecular side-chain van der Waals as only input, LLSF(b): Linear Least Squares Fit with inter-molecular side-chain van der Waals and hydrogen bond as inputs. RMSE stands for root mean squared error, r for correlation coefficient and MCC for Matthews correlation coefficient (see Methods section for definition of the performance measures).