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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2019
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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. |
format | Online Article Text |
id | pubmed-6944713 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
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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