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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
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BMC Bioinformatics

ISSN: 1471-2105

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