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A capsule network-based method for identifying transcription factors
Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763301/ https://www.ncbi.nlm.nih.gov/pubmed/36560938 http://dx.doi.org/10.3389/fmicb.2022.1048478 |
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author | Zheng, Peijie Qi, Yue Li, Xueyong Liu, Yuewu Yao, Yuhua Huang, Guohua |
author_facet | Zheng, Peijie Qi, Yue Li, Xueyong Liu, Yuewu Yao, Yuhua Huang, Guohua |
author_sort | Zheng, Peijie |
collection | PubMed |
description | Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a capsule network-based method for identifying TFs. This method is an end-to-end deep learning method, consisting mainly of an embedding layer, bidirectional long short-term memory (LSTM) layer, capsule network layer, and three fully connected layers. The presented method obtained an accuracy of 0.8820, being superior to the state-of-the-art methods. These empirical experiments showed that the inclusion of the capsule network promoted great performances and that the capsule network-based representation was superior to the property-based representation for distinguishing between TFs and non-TFs. We also implemented the presented method into a user-friendly web server, which is freely available at http://www.biolscience.cn/Capsule_TF/ for all scientific researchers. |
format | Online Article Text |
id | pubmed-9763301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97633012022-12-21 A capsule network-based method for identifying transcription factors Zheng, Peijie Qi, Yue Li, Xueyong Liu, Yuewu Yao, Yuhua Huang, Guohua Front Microbiol Microbiology Transcription factors (TFs) are typical regulators for gene expression and play versatile roles in cellular processes. Since it is time-consuming, costly, and labor-intensive to detect it by using physical methods, it is desired to develop a computational method to detect TFs. Here, we presented a capsule network-based method for identifying TFs. This method is an end-to-end deep learning method, consisting mainly of an embedding layer, bidirectional long short-term memory (LSTM) layer, capsule network layer, and three fully connected layers. The presented method obtained an accuracy of 0.8820, being superior to the state-of-the-art methods. These empirical experiments showed that the inclusion of the capsule network promoted great performances and that the capsule network-based representation was superior to the property-based representation for distinguishing between TFs and non-TFs. We also implemented the presented method into a user-friendly web server, which is freely available at http://www.biolscience.cn/Capsule_TF/ for all scientific researchers. Frontiers Media S.A. 2022-12-06 /pmc/articles/PMC9763301/ /pubmed/36560938 http://dx.doi.org/10.3389/fmicb.2022.1048478 Text en Copyright © 2022 Zheng, Qi, Li, Liu, Yao and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Zheng, Peijie Qi, Yue Li, Xueyong Liu, Yuewu Yao, Yuhua Huang, Guohua A capsule network-based method for identifying transcription factors |
title | A capsule network-based method for identifying transcription factors |
title_full | A capsule network-based method for identifying transcription factors |
title_fullStr | A capsule network-based method for identifying transcription factors |
title_full_unstemmed | A capsule network-based method for identifying transcription factors |
title_short | A capsule network-based method for identifying transcription factors |
title_sort | capsule network-based method for identifying transcription factors |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763301/ https://www.ncbi.nlm.nih.gov/pubmed/36560938 http://dx.doi.org/10.3389/fmicb.2022.1048478 |
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