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Table 2 MCC and ACC of adaptive LSTM and other three methods

From: Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter

Dataset

Metrics

ProbKnot

Cylofold

CentroidFold

Adaptive

Filter

TMR

MCC

0.105

−0.043

0.106

0.434

0.581

ACC

0.531

0.485

0.561

0.630

0.786

SPR

MCC

0.591

*

0.668

0.786

0.751

ACC

0.796

*

0.834

0.891

0.870

SRP

MCC

0.262

−0.184

0.177

0.421

0.475

ACC

0.613

0.396

0.584

0.708

0.690

RFA

MCC

0.398

0.256

0.299

0.451

0.699

ACC

0.677

0.624

0.650

0.661

0.834

ASE

MCC

0.238

0.043

0.286

0.323

0.484

ACC

0.611

0.523

0.642

0.556

0.720

Average

MCC

0.319

0.014

0.307

0.483

0.592

ACC

0.646

0.406

0.654

0.689

0.780

  1. Boldface represents the highest MCC or ACC in comparison with the other three methods
  2. *indicates Cylofold does not generate results on SPR dataset, since Cylofold can not accept the sequence with missing bases in SPR dataset