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DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm

Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classific...

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Detalles Bibliográficos
Autores principales: Yu, Lezheng, Jing, Runyu, Liu, Fengjuan, Luo, Jiesi, Li, Yizhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Gene & Cell Therapy 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658571/
https://www.ncbi.nlm.nih.gov/pubmed/33230481
http://dx.doi.org/10.1016/j.omtn.2020.10.005
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author Yu, Lezheng
Jing, Runyu
Liu, Fengjuan
Luo, Jiesi
Li, Yizhou
author_facet Yu, Lezheng
Jing, Runyu
Liu, Fengjuan
Luo, Jiesi
Li, Yizhou
author_sort Yu, Lezheng
collection PubMed
description Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs.
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spelling pubmed-76585712020-11-17 DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm Yu, Lezheng Jing, Runyu Liu, Fengjuan Luo, Jiesi Li, Yizhou Mol Ther Nucleic Acids Original Article Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs. American Society of Gene & Cell Therapy 2020-10-10 /pmc/articles/PMC7658571/ /pubmed/33230481 http://dx.doi.org/10.1016/j.omtn.2020.10.005 Text en © 2020 The Author(s) http://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 Original Article
Yu, Lezheng
Jing, Runyu
Liu, Fengjuan
Luo, Jiesi
Li, Yizhou
DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
title DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
title_full DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
title_fullStr DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
title_full_unstemmed DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
title_short DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
title_sort deepacp: a novel computational approach for accurate identification of anticancer peptides by deep learning algorithm
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658571/
https://www.ncbi.nlm.nih.gov/pubmed/33230481
http://dx.doi.org/10.1016/j.omtn.2020.10.005
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