Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches
Fig 4
Comparison between NABind and our previous methods (i.e. DNABind and RBRDetector).
(A) Differences in the design strategy of each module. The feature-based module adopted different feature representations and supervised learning models. The template-based module utilized different approaches for constructing the template library and inferring binding residues based on retrieved templates. The integration module used the stacking strategy instead of a piecewise function. A newly designed post-processing module was used in the updated method. (B) Comparison of improved and traditional residue representations using random forest classifiers. (C) Performance comparison of NABindDL, DNABindML and RBRDetectorML. (D) Statistics of best templates retrieved by current and previous methods. (E) Performance comparison of NABindTL, DNABindTL and RBRDetectorTL. (F) Performance comparison of NABind and our previous methods.