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Microbial Dark Matter: from Discovery to Applications

With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynam...

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Detalles Bibliográficos
Autores principales: Zha, Yuguo, Chong, Hui, Yang, Pengshuo, Ning, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025686/
https://www.ncbi.nlm.nih.gov/pubmed/35477055
http://dx.doi.org/10.1016/j.gpb.2022.02.007
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author Zha, Yuguo
Chong, Hui
Yang, Pengshuo
Ning, Kang
author_facet Zha, Yuguo
Chong, Hui
Yang, Pengshuo
Ning, Kang
author_sort Zha, Yuguo
collection PubMed
description With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment.
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spelling pubmed-100256862023-03-21 Microbial Dark Matter: from Discovery to Applications Zha, Yuguo Chong, Hui Yang, Pengshuo Ning, Kang Genomics Proteomics Bioinformatics Review With the rapid increase of the microbiome samples and sequencing data, more and more knowledge about microbial communities has been gained. However, there is still much more to learn about microbial communities, including billions of novel species and genes, as well as countless spatiotemporal dynamic patterns within the microbial communities, which together form the microbial dark matter. In this work, we summarized the dark matter in microbiome research and reviewed current data mining methods, especially artificial intelligence (AI) methods, for different types of knowledge discovery from microbial dark matter. We also provided case studies on using AI methods for microbiome data mining and knowledge discovery. In summary, we view microbial dark matter not as a problem to be solved but as an opportunity for AI methods to explore, with the goal of advancing our understanding of microbial communities, as well as developing better solutions to global concerns about human health and the environment. Elsevier 2022-10 2022-04-26 /pmc/articles/PMC10025686/ /pubmed/35477055 http://dx.doi.org/10.1016/j.gpb.2022.02.007 Text en © 2022 The Authors. Published by Elsevier B.V. and Science Press on behalf of Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China. https://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 Review
Zha, Yuguo
Chong, Hui
Yang, Pengshuo
Ning, Kang
Microbial Dark Matter: from Discovery to Applications
title Microbial Dark Matter: from Discovery to Applications
title_full Microbial Dark Matter: from Discovery to Applications
title_fullStr Microbial Dark Matter: from Discovery to Applications
title_full_unstemmed Microbial Dark Matter: from Discovery to Applications
title_short Microbial Dark Matter: from Discovery to Applications
title_sort microbial dark matter: from discovery to applications
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025686/
https://www.ncbi.nlm.nih.gov/pubmed/35477055
http://dx.doi.org/10.1016/j.gpb.2022.02.007
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