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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...

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Detalles Bibliográficos
Autores principales: Han, Jiyun, Zhang, Shizhuo, Liu, Juntao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
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)
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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
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