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Table 4 The performance of protein domain prediction with out-of-distribution examples using ProDOMA with Outlier Exposure (OE), and HMMER on the real PacBio and Nanopore dataset

From: Improving protein domain classification for third-generation sequencing reads using deep learning

Proportion

Method

PacBio

Nanopore

  

Recall

Precision

F1-score

Recall

Precision

F1-score

1% GPCR

HMMER

0.1403

0.9574

0.2446

0.3902

0.9876

0.5593

 

ProDOMA

0.4581

0.9872

0.6258

0.4727

0.9796

0.6376

5% GPCR

HMMER

0.1537

0.9411

0.2642

0.4015

0.9754

0.5513

 

ProDOMA

0.4712

0.9921

0.6389

0.4615

0.9872

0.6289

10% GPCR

HMMER

0.1491

0.9513

0.2577

0.3988

0.9917

0.5688

 

ProDOMA

0.4473

0.9842

0.6150

0.4886

0.9843

0.6529

50% GPCR

HMMER

0.1494

0.9507

0.2583

0.3984

0.9825

0.5670

 

ProDOMA

0.4479

0.9837

0.6154

0.4836

0.9731

0.6458