Table 3 The prediction performance of the proposed method under the independent dataset test.

From: A deep learning framework combined with word embedding to identify DNA replication origins

Sequence segmentation

Dataset

Training mode

Acc

Sp

Sn

MCC

Continuous-TSSS

S. cerevisiae (\(S_{1}\))

Default mode

0.9559

0.9697

0.9429

0.9122

Embedding training mode

0.9412

0.9697

0.9143

0.8840

Two channel mode

0.9559

1.0000

0.9143

0.9155

S. pombe (\(S_{2}\))

Default mode

0.8116

0.8788

0.7500

0.6315

Embedding training mode

0.7826

0.8182

0.7500

0.5682

Two channel mode

0.8116

0.9091

0.7222

0.6389

K. lactis (\(S_{3}\))

Default mode

0.9000

0.8462

0.9412

0.7964

Embedding training mode

0.8667

0.8462

0.8824

0.7285

Two channel mode

0.8333

0.8182

0.8421

0.6495

P. pastoris (\(S_{4}\))

Default mode

0.9500

0.9444

0.9583

0.8971

Embedding training mode

0.9167

0.9167

0.9167

0.8281

Two channel mode

0.9032

0.8947

0.9167

0.8009

Skip-TSSS

S. cerevisiae (\(S_{1}\))

Default mode

0.9901

0.9794

1.0000

0.9804

Embedding training mode

0.9901

0.9897

0.9906

0.9803

Two channel mode

0.9951

0.9897

1.0000

0.9902

S. pombe (\(S_{2}\))

Default mode

0.7282

0.7353

0.7212

0.4564

Embedding training mode

0.7379

0.6176

0.8558

0.4880

Two channel mode

0.7573

0.7843

0.7308

0.5157

K. lactis (\(S_{3}\))

Default mode

0.9213

0.8667

0.9773

0.8482

Embedding training mode

0.9213

0.8667

0.9773

0.8482

Two channel mode

0.8539

0.9111

0.7955

0.7120

P. pastoris (\(S_{4}\))

Default mode

0.9454

0.9318

0.9579

0.8907

Embedding training mode

0.9727

0.9659

0.9789

0.9453

Two channel mode

0.9617

0.9432

0.9789

0.9238