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Table 3 Comparison with other algorithms

From: Research on RNA secondary structure predicting via bidirectional recurrent neural network

Dataset

Method

SEN

PPV

ACC

MCC

SPR

VLDB

0.962

0.885

0.921

0.845

SVM

0.788

0.856

0.834

0.667

ProbKnot

0.793

0.744

0.772

0.546

LSTM

0.703

0.71

0.687

0.372

Cylofold

*

*

*

*

ASE

VLDB

0.826

0.652

0.727

0.475

SVM

0.712

0.663

0.68

0.361

ProbKnot

0.734

0.564

0.613

0.247

LSTM

0.81

0.739

0.574

0.786

Cylofold

0.66

0.575

0.65

0.299

RFA

VLDB

0.811

0.699

0.778

0.558

SVM

0.151

0.748

0.581

0.182

ProbKnot

0.793

0.555

0.648

0.339

LSTM

0.794

0.54

0.561

0.141

Cylofold

0.667

0.551

0.584

0.177

SRP

VLDB

0.828

0.7

0.729

0.463

SVM

0.682

0.566

0.581

0.167

ProbKnot

0.807

0.598

0.638

0.300

LSTM

0.824

0.665

0.625

0.123

Cylofold

0.673

0.563

0.184

0.589

TMR

VLDB

0.796

0.669

0.765

0.529

SVM

0.498

0.684

0.68

0.34

ProbKnot

0.635

0.388

0.533

0.109

LSTM

0.85

0.538

0.569

0.18

Cylofold

0.526

0.433

0.561

0.106

  1. The data in bold, italics, underline represent the optimal evaluation index values obtained by different algorithms on the same data set
  2. *Cylofold algorithm is unable to measure results on SPR data sets with many base deletion sequences