iACVP-MR : Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP.
METHODS: In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively.
RESULTS: The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models.
CONCLUSION: The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Current medicinal chemistry - (2024) vom: 15. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liang, Yunyun [VerfasserIn] |
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| Links: |
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| Themen: |
Anti-coronavirus peptide |
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| Anmerkungen: |
Date Revised 29.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.2174/0109298673277663240101111507 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370390571 |
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| 245 | 1 | 0 | |a iACVP-MR |b Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 29.03.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infection drugs with clear function, low cost, and high safety. Anti-coronavirus peptide (ACVP) is a key therapeutic agent against coronavirus. Traditional methods for finding ACVP need a great deal of money and man power. Hence, it is a significant task to establish intelligent computational tools to able rapid, efficient and accurate identification of ACVP | ||
| 520 | |a METHODS: In this paper, we construct an excellent model named iACVP-MR to identify ACVP based on multiple features and recurrent neural networks. Multiple features are extracted by using reduced amino acid component and dipeptide component, compositions of k-spaced amino acid pairs, BLOSUM62 encoder according to the N5C5 sequence, as well as second-order moving average approach based on 16 physicochemical properties. Then, two recurrent neural networks named long-short term memory (LSTM) and bidirectional gated recurrent unit (BiGRU) combined attention mechanism are used for feature fusion and classification, respectively | ||
| 520 | |a RESULTS: The accuracies of ENNAVIA-C and ENNAVIA-D datasets under the 10-fold cross-validation are 99.15% and 98.92%, respectively, and other evaluation indexes have also obtained satisfactory results. The experimental results show that our model is superior to other existing models | ||
| 520 | |a CONCLUSION: The iACVP-MR model can be viewed as a powerful and intelligent tool for the accurate identification of ACVP. The datasets and source codes for iACVP-MR are freely downloaded at https://github.com/yunyunliang88/iACVP-MR | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Anti-coronavirus peptide | |
| 650 | 4 | |a attention mechanism. | |
| 650 | 4 | |a bidirectional gated recurrent unit | |
| 650 | 4 | |a long-short term memory | |
| 650 | 4 | |a multiple features | |
| 650 | 4 | |a recurrent neural network | |
| 700 | 1 | |a Ma, Xinyan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shengli |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jin |e verfasserin |4 aut | |
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