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Table 1 Predictive performance of machine-learning models

From: A modular kernel approach for integrative analysis of protein domain boundaries

Models TNf TPf Sensitivity
(Sn)
Specificity
(Sp)
Correlation-
Coefficient (Cc)
Accuracy
(Ac)
HMEHE 0.77 ± 0.015 0.79 ± 0.026 0.78 ± 0.002 0.78 ± 0.012 0.56 ± 0.016 0.78 ± 0.015
HMEPSSM 0.74 ± 0.019 0.74 ± 0.018 0.75 ± 0.010 0.73 ± 0.045 0.48 ± 0.023 0.74 ± 0.016
SVMHE 0.71 ± 0.008 0.73 ± 0.010 0.70 ± 0.003 0.74 ± 0.017 0.44 ± 0.011 0.72 ± 0.020
SVMPSSM 0.71 ± 0.004 0.67 ± 0.008 0.65 ± 0.012 0.72 ± 0.006 0.37 ± 0.007 0.69 ± 0.003
MLPHE 0.69 ± 0.009 0.72 ± 0.012 0.61 ± 0.027 0.75 ± 0.019 0.40 ± 0.013 0.70 ± 0.025
MLPPSSM 0.67 ± 0.017 0.71 ± 0.032 0.61 ± 0.013 0.76 ± 0.027 0.37 ± 0.022 0.68 ± 0.011
  1. Mean testing data ± standard deviation obtained using ANOVA test using optimal settings for each model.