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Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture

Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we...

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Autores principales: Le, Nguyen Quoc Khanh, Yapp, Edward Kien Yee, Nagasundaram, N., Chua, Matthew Chin Heng, Yeh, Hui-Yuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944713/
https://www.ncbi.nlm.nih.gov/pubmed/31921391
http://dx.doi.org/10.1016/j.csbj.2019.09.005
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author Le, Nguyen Quoc Khanh
Yapp, Edward Kien Yee
Nagasundaram, N.
Chua, Matthew Chin Heng
Yeh, Hui-Yuan
author_facet Le, Nguyen Quoc Khanh
Yapp, Edward Kien Yee
Nagasundaram, N.
Chua, Matthew Chin Heng
Yeh, Hui-Yuan
author_sort Le, Nguyen Quoc Khanh
collection PubMed
description Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we suggested a new strategy that includes gated recurrent units and position-specific scoring matrix profiles to predict vesicular transportation proteins, a biological function of great importance. Although it is difficult to discover its function, our model is able to achieve accuracies of 82.3% and 85.8% in the cross-validation and independent dataset, respectively. We also solve the problem of imbalance in the dataset via tuning class weight in the deep learning model. The results generated showed sensitivity, specificity, MCC, and AUC to have values of 79.2%, 82.9%, 0.52, and 0.861, respectively. Our strategy shows superiority in results on the same dataset against all other state-of-the-art algorithms. In our suggested research, we have suggested a technique for the discovery of more proteins, particularly proteins connected with vesicular transport. In addition, our accomplishment could encourage the use of gated recurrent units architecture in protein function prediction.
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spelling pubmed-69447132020-01-09 Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture Le, Nguyen Quoc Khanh Yapp, Edward Kien Yee Nagasundaram, N. Chua, Matthew Chin Heng Yeh, Hui-Yuan Comput Struct Biotechnol J Research Article Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we suggested a new strategy that includes gated recurrent units and position-specific scoring matrix profiles to predict vesicular transportation proteins, a biological function of great importance. Although it is difficult to discover its function, our model is able to achieve accuracies of 82.3% and 85.8% in the cross-validation and independent dataset, respectively. We also solve the problem of imbalance in the dataset via tuning class weight in the deep learning model. The results generated showed sensitivity, specificity, MCC, and AUC to have values of 79.2%, 82.9%, 0.52, and 0.861, respectively. Our strategy shows superiority in results on the same dataset against all other state-of-the-art algorithms. In our suggested research, we have suggested a technique for the discovery of more proteins, particularly proteins connected with vesicular transport. In addition, our accomplishment could encourage the use of gated recurrent units architecture in protein function prediction. Research Network of Computational and Structural Biotechnology 2019-10-25 /pmc/articles/PMC6944713/ /pubmed/31921391 http://dx.doi.org/10.1016/j.csbj.2019.09.005 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Le, Nguyen Quoc Khanh
Yapp, Edward Kien Yee
Nagasundaram, N.
Chua, Matthew Chin Heng
Yeh, Hui-Yuan
Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
title Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
title_full Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
title_fullStr Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
title_full_unstemmed Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
title_short Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
title_sort computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944713/
https://www.ncbi.nlm.nih.gov/pubmed/31921391
http://dx.doi.org/10.1016/j.csbj.2019.09.005
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