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Table 8 Comparison of the performance of the proposed method with other representations in the literature

From: New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning

Performance

ASCII

EIIP

One-hot

This work

Metrics

[36]

[37]

Encoding

[38]

\(M=64\)

\(M=128\)

\(M=256\)

Processing time p/sequence

0.0180 s

0.0181 s

0.648 s

0.0063 s

0.0063 s

0.0064 s

Memory required p/1000 vectors

8.86 MB

16.8 MB

16.9 MB

471 KB

941 KB

1.83 MB

Training time per fold

48.4 min

46.55 min

54.5 min

12 s

14 s

17 s

ACC

\(98\%\)

\(98.5\%\)

\(96\%\)

\(98.35\%\)

\(99.08\%\)

\(99.69\%\)

SEN

\(96.1\%\)

\(98\%\)

\(98\%\)

\(98.40\%\)

\(99.13\%\)

\(99.74\%\)

SPE

\(100\%\)

\(99\%\)

\(94.2\%\)

\(93.06\%\)

\(93.88\%\)

\(94.69\%\)

PRE

\(100\%\)

\(99\%\)

\(94.2\%\)

\(99.93\%\)

\(99.94\%\)

\(99.95\%\)

F1-score

\(98.01\%\)

\(98.49\%\)

\(96.06\%\)

\(99.16\%\)

\(99.53\%\)

\(99.84\%\)