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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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. |
format | Online Article Text |
id | pubmed-10025686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhayuguo microbialdarkmatterfromdiscoverytoapplications AT chonghui microbialdarkmatterfromdiscoverytoapplications AT yangpengshuo microbialdarkmatterfromdiscoverytoapplications AT ningkang microbialdarkmatterfromdiscoverytoapplications |