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Table 3 Performance on the ArchiveII dataset (sequence-wise CV)

From: RNA secondary structure prediction with convolutional neural networks

 

Precision

Recall

F1

Mfold

0.428

0.383

0.401

CDPfold

0.557

0.535

0.545

RNAstructure

0.563

0.615

0.585

RNAfold

0.565

0.627

0.592

LinearFold

0.641

0.617

0.621

CONTRAfold

0.607

0.679

0.638

E2Efold

0.734

0.660

0.686

MXFold2 [16]

0.790

0.815

0.800

CNNFold-mix

0.928

0.879

0.897

  1. Bold values are the best result in each column
  2. All trainable models have been trained on a subset of RSA-tr with removing pseudoknotted structures and samples longer than 600 nt
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BMC Bioinformatics

ISSN: 1471-2105

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