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Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction

OBJECTIVES: Antibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs). Recently, we have reported AMPlify, an attentive deep learning model for predicting AMPs i...

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Autores principales: Li, Chenkai, Warren, René L., Birol, Inanc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896668/
https://www.ncbi.nlm.nih.gov/pubmed/36732807
http://dx.doi.org/10.1186/s13104-023-06279-1
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author Li, Chenkai
Warren, René L.
Birol, Inanc
author_facet Li, Chenkai
Warren, René L.
Birol, Inanc
author_sort Li, Chenkai
collection PubMed
description OBJECTIVES: Antibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs). Recently, we have reported AMPlify, an attentive deep learning model for predicting AMPs in databases of peptide sequences. In our tests, AMPlify outperformed the state-of-the-art. We have illustrated its use on data describing the American bullfrog (Rana [Lithobates] catesbeiana) genome. Here we present the model files and training/test data sets we used in that study. The original model (the balanced model) was trained on a balanced set of AMP and non-AMP sequences curated from public databases. In this data note, we additionally provide a model trained on an imbalanced set, in which non-AMP sequences far outnumber AMP sequences. We note that the balanced and imbalanced models would serve different use cases, and both would serve the research community, facilitating the discovery and development of novel AMPs. DATA DESCRIPTION: This data note provides two sets of models, as well as two AMP and four non-AMP sequence sets for training and testing the balanced and imbalanced models. Each model set includes five single sub-models that form an ensemble model. The first model set corresponds to the original model trained on a balanced training set that has been described in the original AMPlify manuscript, while the second model set was trained on an imbalanced training set.
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spelling pubmed-98966682023-02-04 Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction Li, Chenkai Warren, René L. Birol, Inanc BMC Res Notes Data Note OBJECTIVES: Antibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs). Recently, we have reported AMPlify, an attentive deep learning model for predicting AMPs in databases of peptide sequences. In our tests, AMPlify outperformed the state-of-the-art. We have illustrated its use on data describing the American bullfrog (Rana [Lithobates] catesbeiana) genome. Here we present the model files and training/test data sets we used in that study. The original model (the balanced model) was trained on a balanced set of AMP and non-AMP sequences curated from public databases. In this data note, we additionally provide a model trained on an imbalanced set, in which non-AMP sequences far outnumber AMP sequences. We note that the balanced and imbalanced models would serve different use cases, and both would serve the research community, facilitating the discovery and development of novel AMPs. DATA DESCRIPTION: This data note provides two sets of models, as well as two AMP and four non-AMP sequence sets for training and testing the balanced and imbalanced models. Each model set includes five single sub-models that form an ensemble model. The first model set corresponds to the original model trained on a balanced training set that has been described in the original AMPlify manuscript, while the second model set was trained on an imbalanced training set. BioMed Central 2023-02-02 /pmc/articles/PMC9896668/ /pubmed/36732807 http://dx.doi.org/10.1186/s13104-023-06279-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Data Note
Li, Chenkai
Warren, René L.
Birol, Inanc
Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction
title Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction
title_full Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction
title_fullStr Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction
title_full_unstemmed Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction
title_short Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction
title_sort models and data of amplify: a deep learning tool for antimicrobial peptide prediction
topic Data Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896668/
https://www.ncbi.nlm.nih.gov/pubmed/36732807
http://dx.doi.org/10.1186/s13104-023-06279-1
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