Development of a machine‐learning model to identify the impacts of pesticides characteristics on soil microbial communities from high‐throughput sequencing data-Reference-Cited by-同舟云学术

Development of a machine‐learning model to identify the impacts of pesticides characteristics on soil microbial communities from high‐throughput sequencing data

Published:2022-08-28 Issue:11 Volume:24 Page:5561-5573
ISSN:1462-2912
Container-title:Environmental Microbiology
language:en
Short-container-title:Environmental Microbiology

Author:

Ke Mingjing¹,Xu Nuohan¹,Zhang Zhenyan¹,Qiu Danyan¹,Kang Jian¹,Lu Tao¹,Wang Tingzhang²,Peijnenburg W. J. G. M.³⁴,Sun Liwei¹,Hu Baolan⁵^ORCID,Qian Haifeng¹^ORCID

Affiliation:

1. College of Environment Zhejiang University of Technology Hangzhou People's Republic of China

2. Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province Hangzhou People's Republic of China

3. Institute of Environmental Sciences (CML) Leiden University Leiden The Netherlands

4. National Institute of Public Health and the Environment (RIVM) Center for Safety of Substances and Products Bilthoven The Netherlands

5. Department of Environmental Science Zhejiang University Hangzhou People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Ecology, Evolution, Behavior and Systematics,Microbiology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1462-2920.16175

Reference71 articles.

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4. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil

5. High diversity of cultivable heterotrophic bacteria in association with cyanobacterial water blooms

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