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Table 4 The average precision, ranking loss and coverage of model BLSTM, BLSTM + ConvNet1, ConvNet2 and BLSTM + ConvNet1 + ConvNet2

From: Predicting subcellular location of protein with evolution information and sequence-based deep learning

 

D3106

D4802

 

RL

Cov

AP

RL

Cov

AP

BLSTM

0.0967

1.5895

0.7523

0.0820

3.3916

0.6901

BLSTM + ConvNet1

0.0778

1.3113

0.7876

0.0603

2.9225

0.7453

ConvNet2

0.1294

2.0113

0.6430

0.0673

3.2868

0.6214

BLSTM + ConvNet1 + ConvNet2

0.0758

1.2848

0.7901

0.0637

3.0528

0.7414

  1. On dataset D3106, the BLSTM + ConvNet1 + ConvNet2 has the best performance with lowest ranking loss, coverage and highest average precision which are 0.0758, 1.2848 and 0.7901, respectively. However, the model BLSTM + ConvNet1 + ConvNet2 is not as good as model BLSTM + ConvNet1 when tested on dataset D4802. The best values are marked out with bold text
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BMC Bioinformatics

ISSN: 1471-2105

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