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Table 5 The average precision, ranking loss and coverage of IMMMLGP, Hum-mPloc, mGOF-loc, MKSVM, FSVM-KNR and out method when they are tested on datasets D3106 and D4802

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

 

D3106

D4802

 

RL

Cov

AP

RL

Cov

AP

IMMMLGP

0.4190

4.3030

0.5810

0.2436

4.9772

0.5725

Hum-mPloc

0.4906

5.3170

0.5790

0.3145

5.6830

0.5644

mGOF-loc

–

–

–

0.0606

3.0227

0.6482

MKSVM

0.1085

1.7193

0.7065

0.0662

2.9753

0.6889

FSVM-KNR

0.1071

1.7025

0.7108

0.0971

2.6339

0.6916

Our Method

0.0758

1.2848

0.7901

0.0637

3.0528

0.7414

  1. Our method has great improvements on subcellular localization prediction than five currently available methods when tested on dataset D3106. The average precision of our method is 0.7901 which is 0.08 greater than the average precision of FSVM-KNR. The ranking loss and coverage of our method are lower than the values of other five methods. On dataset D4802, our method did not get the lowest ranking loss and coverage. However, the average precision of our method on dataset D4802 is the highest among those six methods with 0.7414. The best values are listed out with bold text
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BMC Bioinformatics

ISSN: 1471-2105

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