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DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm t...

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Detalles Bibliográficos
Autores principales: Cao, Ruifen, Wang, Meng, Bin, Yannan, Zheng, Chunhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344685/
https://www.ncbi.nlm.nih.gov/pubmed/34414035
http://dx.doi.org/10.7717/peerj.11906
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author Cao, Ruifen
Wang, Meng
Bin, Yannan
Zheng, Chunhou
author_facet Cao, Ruifen
Wang, Meng
Bin, Yannan
Zheng, Chunhou
author_sort Cao, Ruifen
collection PubMed
description An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.
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spelling pubmed-83446852021-08-18 DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion Cao, Ruifen Wang, Meng Bin, Yannan Zheng, Chunhou PeerJ Bioinformatics An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP. PeerJ Inc. 2021-08-03 /pmc/articles/PMC8344685/ /pubmed/34414035 http://dx.doi.org/10.7717/peerj.11906 Text en ©2021 Cao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Cao, Ruifen
Wang, Meng
Bin, Yannan
Zheng, Chunhou
DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion
title DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion
title_full DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion
title_fullStr DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion
title_full_unstemmed DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion
title_short DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion
title_sort dlff-acp: prediction of acps based on deep learning and multi-view features fusion
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344685/
https://www.ncbi.nlm.nih.gov/pubmed/34414035
http://dx.doi.org/10.7717/peerj.11906
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