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Application of Deep Learning in Plant–Microbiota Association Analysis
Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of mic...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531731/ https://www.ncbi.nlm.nih.gov/pubmed/34691142 http://dx.doi.org/10.3389/fgene.2021.697090 |
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author | Deng, Zhiyu Zhang, Jinming Li, Junya Zhang, Xiujun |
author_facet | Deng, Zhiyu Zhang, Jinming Li, Junya Zhang, Xiujun |
author_sort | Deng, Zhiyu |
collection | PubMed |
description | Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis. |
format | Online Article Text |
id | pubmed-8531731 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85317312021-10-23 Application of Deep Learning in Plant–Microbiota Association Analysis Deng, Zhiyu Zhang, Jinming Li, Junya Zhang, Xiujun Front Genet Genetics Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis. Frontiers Media S.A. 2021-10-08 /pmc/articles/PMC8531731/ /pubmed/34691142 http://dx.doi.org/10.3389/fgene.2021.697090 Text en Copyright © 2021 Deng, Zhang, Li and Zhang. 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 | Genetics Deng, Zhiyu Zhang, Jinming Li, Junya Zhang, Xiujun Application of Deep Learning in Plant–Microbiota Association Analysis |
title | Application of Deep Learning in Plant–Microbiota Association Analysis |
title_full | Application of Deep Learning in Plant–Microbiota Association Analysis |
title_fullStr | Application of Deep Learning in Plant–Microbiota Association Analysis |
title_full_unstemmed | Application of Deep Learning in Plant–Microbiota Association Analysis |
title_short | Application of Deep Learning in Plant–Microbiota Association Analysis |
title_sort | application of deep learning in plant–microbiota association analysis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531731/ https://www.ncbi.nlm.nih.gov/pubmed/34691142 http://dx.doi.org/10.3389/fgene.2021.697090 |
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