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MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation
Methylation is an important epigenetic regulation of methylation genes that plays a crucial role in regulating biological processes. While traditional methods for detecting methylation in biological experiments are constantly improving, the development of artificial intelligence has led to the emerg...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564812/ https://www.ncbi.nlm.nih.gov/pubmed/37648910 http://dx.doi.org/10.1007/s12064-023-00402-3 |
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author | Bai, JianGuo Yang, Hai Wu, ChangDe |
author_facet | Bai, JianGuo Yang, Hai Wu, ChangDe |
author_sort | Bai, JianGuo |
collection | PubMed |
description | Methylation is an important epigenetic regulation of methylation genes that plays a crucial role in regulating biological processes. While traditional methods for detecting methylation in biological experiments are constantly improving, the development of artificial intelligence has led to the emergence of deep learning and machine learning methods as a new trend. However, traditional machine learning-based methods rely heavily on manual feature extraction, and most deep learning methods for studying methylation extract fewer features due to their simple network structures. To address this, we propose a bottomneck network based on an attention mechanism and use new methods to ensure that the deep network can learn more effective features while minimizing overfitting. This approach enables the model to learn more features from nucleotide sequences and make better predictions of methylation. The model uses three coding methods to encode the original DNA sequence and then applies feature fusion based on attention mechanisms to obtain the best fusion method. Our results demonstrate that MLACNN outperforms previous methods and achieves more satisfactory performance. |
format | Online Article Text |
id | pubmed-10564812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-105648122023-10-12 MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation Bai, JianGuo Yang, Hai Wu, ChangDe Theory Biosci Original Article Methylation is an important epigenetic regulation of methylation genes that plays a crucial role in regulating biological processes. While traditional methods for detecting methylation in biological experiments are constantly improving, the development of artificial intelligence has led to the emergence of deep learning and machine learning methods as a new trend. However, traditional machine learning-based methods rely heavily on manual feature extraction, and most deep learning methods for studying methylation extract fewer features due to their simple network structures. To address this, we propose a bottomneck network based on an attention mechanism and use new methods to ensure that the deep network can learn more effective features while minimizing overfitting. This approach enables the model to learn more features from nucleotide sequences and make better predictions of methylation. The model uses three coding methods to encode the original DNA sequence and then applies feature fusion based on attention mechanisms to obtain the best fusion method. Our results demonstrate that MLACNN outperforms previous methods and achieves more satisfactory performance. Springer Berlin Heidelberg 2023-08-30 2023 /pmc/articles/PMC10564812/ /pubmed/37648910 http://dx.doi.org/10.1007/s12064-023-00402-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Bai, JianGuo Yang, Hai Wu, ChangDe MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation |
title | MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation |
title_full | MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation |
title_fullStr | MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation |
title_full_unstemmed | MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation |
title_short | MLACNN: an attention mechanism-based CNN architecture for predicting genome-wide DNA methylation |
title_sort | mlacnn: an attention mechanism-based cnn architecture for predicting genome-wide dna methylation |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564812/ https://www.ncbi.nlm.nih.gov/pubmed/37648910 http://dx.doi.org/10.1007/s12064-023-00402-3 |
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