Skip to main content

Table 5 Performance by various propensity integration methods

From: Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences

Group Methods Propensities Recall Precision Fscore
Voting Ranked votinga 209 0.650 0.191 0.295
  Exhaustive voting 5 groups 0.656 0.191 0.295
General SVM 5 groups 0.614 0.181 0.279
  Random forest 5 groups 0.600 0.186 0.284
Bayesian Naïve Bayesian 5 groups 0.686 0.193 0.301
  Bayesian network 5 groups 0.775 0.173 0.283
Tree Decision tree 5 groups 0.675 0.196 0.304
  Regression tree 5 groups 0.655 0.194 0.299
General SVM 209 0.594 0.191 0.289
  Random forest 209 0.639 0.191 0.294
Bayesian Naïve Bayesian 209 0.633 0.198 0.301
  Bayesian network 209 0.586 0.170 0.264
Tree Decision tree 209 0.633 0.198 0.301
  1. The parameters used in these models are tuned to realize an optimal performance. The best ranked voting was achieved by propensity PSSM (P209), ASA (P206) and optimized beta-structure-coil equilibrium constant (P170, AAIndex ID: OOBM850101). The best exhaustive voting results was realized by a combination of PSSM, physico-chemical propensities and ASA
  2. aDue to the complexity of exhaustive voting algorithm, only five groups of propensities were used. Regression tree constructed on 209 propensities required too much computation time, and no results were obtained