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TOXIFY: a deep learning approach to classify animal venom proteins

In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we traine...

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Detalles Bibliográficos
Autores principales: Cole, T. Jeffrey, Brewer, Michael S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6601600/
https://www.ncbi.nlm.nih.gov/pubmed/31293833
http://dx.doi.org/10.7717/peerj.7200
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author Cole, T. Jeffrey
Brewer, Michael S.
author_facet Cole, T. Jeffrey
Brewer, Michael S.
author_sort Cole, T. Jeffrey
collection PubMed
description In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins.
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spelling pubmed-66016002019-07-10 TOXIFY: a deep learning approach to classify animal venom proteins Cole, T. Jeffrey Brewer, Michael S. PeerJ Bioinformatics In the era of Next-Generation Sequencing and shotgun proteomics, the sequences of animal toxigenic proteins are being generated at rates exceeding the pace of traditional means for empirical toxicity verification. To facilitate the automation of toxin identification from protein sequences, we trained Recurrent Neural Networks with Gated Recurrent Units on publicly available datasets. The resulting models are available via the novel software package TOXIFY, allowing users to infer the probability of a given protein sequence being a venom protein. TOXIFY is more than 20X faster and uses over an order of magnitude less memory than previously published methods. Additionally, TOXIFY is more accurate, precise, and sensitive at classifying venom proteins. PeerJ Inc. 2019-06-28 /pmc/articles/PMC6601600/ /pubmed/31293833 http://dx.doi.org/10.7717/peerj.7200 Text en ©2019 Cole and Brewer http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Cole, T. Jeffrey
Brewer, Michael S.
TOXIFY: a deep learning approach to classify animal venom proteins
title TOXIFY: a deep learning approach to classify animal venom proteins
title_full TOXIFY: a deep learning approach to classify animal venom proteins
title_fullStr TOXIFY: a deep learning approach to classify animal venom proteins
title_full_unstemmed TOXIFY: a deep learning approach to classify animal venom proteins
title_short TOXIFY: a deep learning approach to classify animal venom proteins
title_sort toxify: a deep learning approach to classify animal venom proteins
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6601600/
https://www.ncbi.nlm.nih.gov/pubmed/31293833
http://dx.doi.org/10.7717/peerj.7200
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AT brewermichaels toxifyadeeplearningapproachtoclassifyanimalvenomproteins