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Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network
Here, we describe the use of TriNet to predict peptides with anticancer and antimicrobial properties by a tri-fusion neural network. We detail the use of TriNet for both the offline Python script version and the online service, thereby demonstrating its convenience for users. In addition, we provide...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491854/ https://www.ncbi.nlm.nih.gov/pubmed/37660298 http://dx.doi.org/10.1016/j.xpro.2023.102541 |
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author | Han, Jiyun Zhang, Shizhuo Liu, Juntao |
author_facet | Han, Jiyun Zhang, Shizhuo Liu, Juntao |
author_sort | Han, Jiyun |
collection | PubMed |
description | Here, we describe the use of TriNet to predict peptides with anticancer and antimicrobial properties by a tri-fusion neural network. We detail the use of TriNet for both the offline Python script version and the online service, thereby demonstrating its convenience for users. In addition, we provide a detailed explanation of the training process of TriNet to enhance the understanding of researchers seeking to leverage deep learning techniques for peptide classification. For complete details on the use and execution of this protocol, please refer to Zhou et al.(1) |
format | Online Article Text |
id | pubmed-10491854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104918542023-09-10 Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network Han, Jiyun Zhang, Shizhuo Liu, Juntao STAR Protoc Protocol Here, we describe the use of TriNet to predict peptides with anticancer and antimicrobial properties by a tri-fusion neural network. We detail the use of TriNet for both the offline Python script version and the online service, thereby demonstrating its convenience for users. In addition, we provide a detailed explanation of the training process of TriNet to enhance the understanding of researchers seeking to leverage deep learning techniques for peptide classification. For complete details on the use and execution of this protocol, please refer to Zhou et al.(1) Elsevier 2023-09-02 /pmc/articles/PMC10491854/ /pubmed/37660298 http://dx.doi.org/10.1016/j.xpro.2023.102541 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Han, Jiyun Zhang, Shizhuo Liu, Juntao Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network |
title | Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network |
title_full | Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network |
title_fullStr | Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network |
title_full_unstemmed | Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network |
title_short | Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network |
title_sort | protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10491854/ https://www.ncbi.nlm.nih.gov/pubmed/37660298 http://dx.doi.org/10.1016/j.xpro.2023.102541 |
work_keys_str_mv | AT hanjiyun protocolforpredictingpeptideswithanticancerandantimicrobialpropertiesbyatrifusionneuralnetwork AT zhangshizhuo protocolforpredictingpeptideswithanticancerandantimicrobialpropertiesbyatrifusionneuralnetwork AT liujuntao protocolforpredictingpeptideswithanticancerandantimicrobialpropertiesbyatrifusionneuralnetwork |