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微生物学家的机器学习。

Machine learning for microbiologists.

机构信息

Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.

Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.

出版信息

Nat Rev Microbiol. 2024 Apr;22(4):191-205. doi: 10.1038/s41579-023-00984-1. Epub 2023 Nov 15.

Abstract

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

摘要

机器学习在微生物学中越来越重要,它被用于预测抗生素耐药性以及将人类微生物组特征与复杂的宿主疾病相关联等任务。在微生物学中的应用正在迅速扩展,基础和临床研究中常用的机器学习工具从分类和回归到聚类和降维。在这篇综述中,我们研究了与实验和临床微生物学家相关的主要机器学习概念、任务和应用。我们为微生物学家提供了一个基本工具包,使他们能够在实验和转化活动中理解、解释和使用机器学习。

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Machine learning for microbiologists.微生物学家的机器学习。
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Front Cell Infect Microbiol. 2025 May 1;15:1545646. doi: 10.3389/fcimb.2025.1545646. eCollection 2025.

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Selective Inference for Hierarchical Clustering.层次聚类的选择性推断
J Am Stat Assoc. 2024;119(545):332-342. doi: 10.1080/01621459.2022.2116331. Epub 2022 Oct 11.

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