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IET systems biology. 2025 Jan-Dec;19(1):e70009. doi: 10.1049/syb2.70009 Q41.92024

Investigating the Impact of Antibiotics on Environmental Microbiota Through Machine Learning Models

通过机器学习模型调查抗生素对环境微生物的影响 翻译改进

Yiheng Du  1, Khandaker Asif Ahmed  2, Md Rakibul Hasan  3  4, Md Zakir Hossain  1  4

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作者单位

  • 1 Australian National University, Canberra, Australia.
  • 2 CSIRO Australian Centre for Disease Prepardness, Geelong, Australia.
  • 3 BRAC University, Dhaka, Bangladesh.
  • 4 Curtin University, Bentley, Australia.
  • DOI: 10.1049/syb2.70009 PMID: 40150863

    摘要 Ai翻译

    Antibiotic pollution in the environment can significantly impact soil microorganisms, such as altering the soil microbial community or emerging antibiotic-resistant bacteria. We propose three machine learning (ML) methods to investigate antibiotics' impact on microorganisms and predict microbial abundance. We examined the microbial abundances of various environmental soil samples treated with antibiotics. We developed 3 ML models: (Model 1) for predicting the most abundant bacterial classes in a specific treatment group; (Model 2) for predicting antibiotic treatment effects based on bacterial abundances; and (Model 3) for using data from short-term incubations to predict the data of community structure after stabilisation. In Model 1, the Random Forest model achieved the highest average accuracy, with a Coefficient of Variation mean of 0.05 and 0.14 in the training and test set. In Model 2, the accuracy of the random forest and SVM models have the highest accuracy (nearly 0.90). Model 3 demonstrates that the Random Forest can use data from short-term incubations to predict the abundance of bacterial communities after long-term stabilisation. This study highlights the potential of ML models as powerful tools for understanding microbial dynamics in response to antibiotic treatments. The code is publicly available at - https://github.com/DeweyYihengDu/ML_on_Microbiota.

    Keywords: bioinformatics; biology computing; learning (artificial intelligence).

    Keywords:environmental microbiota; machine learning models; impact of antibiotics

    Copyright © IET systems biology. 中文内容为AI机器翻译,仅供参考!

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    期刊名:Iet systems biology

    缩写:IET SYST BIOL

    ISSN:1751-8849

    e-ISSN:1751-8857

    IF/分区:1.9/Q4

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