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Table 3 Performance accuracies of different methods in predicting donor splice sites using NN269 dataset

From: Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features

Approaches AUC-ROC AUC-PR Type of kernel used
MM1-SVM 97.62 89.58 Polynomial
LIK-SVM 98.04 92.65 Locally improved kernel
WD-SVM 98.50 92.86 Weighted degree kernel
WDS-SVM 98.13 92.47 Weighted degree shift kernel
EFFECT 98.20 92.81
Proposed 96.53 93.54 Radial basis function
  1. It can be seen that WD-SVM achieved higher value of AUC-ROC as compared to the others, whereas the AUC-PR is highest for the proposed approach. MM1-SVM achieved lowest accuracies both in terms of AUC-ROC and AUC-PR