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Table 1 Performance of different manifold learning methods on the test set

From: Prediction of hot spots in protein–DNA binding interfaces based on supervised isometric feature mapping and extreme gradient boosting

Method SEN SPE PRE F1 ACC MCC AUC
LLE (10) 0.653 0.711 0.607 0.629 0.687 0.361 0.693
ISOMAP (10) 0.687 0.766 0.692 0.695 0.709 0.476 0.738
SLLE (3) 0.671 0.732 0.648 0.656 0.691 0.381 0.703
S-ISOMAP (3) 0.707 0.819 0.721 0.713 0.768 0.508 0.773
  1. The highest value in each column is shown in bold. The numbers in parentheses represent the feature dimensions after dimensionality reduction