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Table 1 Summary of results for the binary classification problem

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

  Model Precision Recall F1 score MCC
(a) SVM 0.53 ± 0.03 0.63 ± 0.04 0.58 ± 0.02 0.44 ± 0.03
  TSVM 0.56 ± 0.03 0.65 ± 0.03 0.60 ± 0.02 0.47 ± 0.02
  GP 0.59 0.32 0.41 0.33
  Robetta2 0.52 0.47 0.49 0.35
  Robetta1.8 0.53 0.52 0.52 0.38
  Robetta1 0.39 0.75 0.52 0.34
(b) SVM 0.64 ± 0.03 0.79 ± 0.05 0.71 ± 0.01 0.40 ± 0.03
  Robetta1 0.69 0.65 0.67 0.38
  1. In (a) experimental hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, in (b) a threshold of 1 kcal/mol is used. SVM: Support Vector Machine, TSVM: Transductive SVM, GP: Gaussian Processes, : Robetta scores (estimated ΔΔG values) which are greater than x th are considered as predicted hot spots. MCC is the Matthews correlation coefficient (see Methods section for definition of the various performance measures). Results for SVM, TSVM and GP have been obtained with a 16-fold cross-validation scheme. Results for Robetta have been retrieved from the server at http://robetta.bakerlab.org/.