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Table 2 Summary of state-of-the-art references

From: Unsupervised clustering of SARS-CoV-2 using deep convolutional autoencoder

Ref

Objective

DNN

Input

Output

Accuracy

[27]

Viral classification

CNN + FC

The whole genomic sequences of a virus

Different viral classes

96.7% (with noise)

98.7% (without noise)

[28]

Viral host prediction

RC-CNN and RC-LSTM

Contigs of a genome

Human or nonhuman host

91.7% CNN

86.3% LSTM

[29]

Viral classification

Stacked sparse autoencoder (SSAE)

Image representations of the complete genome sequences

Different classes

98.9% and 100%

[30]

Predicting the mutation rate of SARS-CoV-2

LSTM-RNN

Complete genome

Mutation rate calculations

(RMSE) of 0.06 in testing and 0.04 in training

[31]

Predicting the similarity score of the genome of “SARS-CoV-2” with other viruses

CNN + LSTM

Genome sequence

Similarity score with other viruses

99.27%