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Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition

Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM)...

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Autores principales: Huang, Guohua, Luo, Wei, Zhang, Guiyang, Zheng, Peijie, Yao, Yuhua, Lyu, Jianyi, Liu, Yuewu, Wei, Dong-Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313278/
https://www.ncbi.nlm.nih.gov/pubmed/35883552
http://dx.doi.org/10.3390/biom12070995
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author Huang, Guohua
Luo, Wei
Zhang, Guiyang
Zheng, Peijie
Yao, Yuhua
Lyu, Jianyi
Liu, Yuewu
Wei, Dong-Qing
author_facet Huang, Guohua
Luo, Wei
Zhang, Guiyang
Zheng, Peijie
Yao, Yuhua
Lyu, Jianyi
Liu, Yuewu
Wei, Dong-Qing
author_sort Huang, Guohua
collection PubMed
description Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers.
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spelling pubmed-93132782022-07-26 Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition Huang, Guohua Luo, Wei Zhang, Guiyang Zheng, Peijie Yao, Yuhua Lyu, Jianyi Liu, Yuewu Wei, Dong-Qing Biomolecules Article Enhancers are short DNA segments that play a key role in biological processes, such as accelerating transcription of target genes. Since the enhancer resides anywhere in a genome sequence, it is difficult to precisely identify enhancers. We presented a bi-directional long-short term memory (Bi-LSTM) and attention-based deep learning method (Enhancer-LSTMAtt) for enhancer recognition. Enhancer-LSTMAtt is an end-to-end deep learning model that consists mainly of deep residual neural network, Bi-LSTM, and feed-forward attention. We extensively compared the Enhancer-LSTMAtt with 19 state-of-the-art methods by 5-fold cross validation, 10-fold cross validation and independent test. Enhancer-LSTMAtt achieved competitive performances, especially in the independent test. We realized Enhancer-LSTMAtt into a user-friendly web application. Enhancer-LSTMAtt is applicable not only to recognizing enhancers, but also to distinguishing strong enhancer from weak enhancers. Enhancer-LSTMAtt is believed to become a promising tool for identifying enhancers. MDPI 2022-07-17 /pmc/articles/PMC9313278/ /pubmed/35883552 http://dx.doi.org/10.3390/biom12070995 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Guohua
Luo, Wei
Zhang, Guiyang
Zheng, Peijie
Yao, Yuhua
Lyu, Jianyi
Liu, Yuewu
Wei, Dong-Qing
Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition
title Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition
title_full Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition
title_fullStr Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition
title_full_unstemmed Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition
title_short Enhancer-LSTMAtt: A Bi-LSTM and Attention-Based Deep Learning Method for Enhancer Recognition
title_sort enhancer-lstmatt: a bi-lstm and attention-based deep learning method for enhancer recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313278/
https://www.ncbi.nlm.nih.gov/pubmed/35883552
http://dx.doi.org/10.3390/biom12070995
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