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Table 3 The F1 score, MCC and AUC of subcellular location prediction generated by 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

 

F1

MCC

AUC

F1

MCC

AUC

BLSTM

0.7473

0.6001

0.9242

0.7419

0.5705

0.9121

BLSTM + ConvNet1

0.7775

0.6419

0.9255

0.7801

0.6284

0.9327

ConvNet2

0.6475

0.4819

0.8785

0.6696

0.4259

0.9297

BLSTM + ConvNet1 + ConvNet2

0.7843

0.6410

0.9458

0.7842

0.6411

0.9434

  1. The four models were tested on datasets D3106 and D4802. On dataset D3106, the highest F1 score and AUC are achieved by the model BLSTM + ConvNet1 + ConvNet2, while the model BLSTM + ConvNet1 has the highest MCC. On dataset D4802, the model BLSTM + ConvNet1 + ConvNet2 was the best among the four models
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BMC Bioinformatics

ISSN: 1471-2105

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