微生物学家的机器学习
Francesco Asnicar1, Andrew Maltez Thomas1, Andrea Passerini2
1Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
Nature reviews. Microbiology
|November 15, 2023
概括
机器学习 (ML) 在微生物学中对于预测抗生素耐药性和理解宿主-微生物组联系至关重要. 本综述为研究人员提供了必要的ML工具,以便他们在实验和临床工作中应用.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 机器学习 (ML) 在微生物学中越来越突出.
- ML有助于预测抗生素耐药性,并将微生物组特征与宿主疾病联系起来.
- 目前的ML应用包括分类,回归,聚类和缩小维度.
研究的目的:
- 审查与微生物学家相关的关键机器学习概念,任务和应用.
- 为实验和临床微生物学家提供ML的基础理解.
- 在微生物学研究中指导ML的解释和应用.
主要方法:
- 审查机器学习概念及其与微生物学相关性的研究.
- 检查常见的ML任务 (分类,回归,聚类,缩小维度).
- 讨论实验和转化微生物学中的实际应用.
主要成果:
- 确定适用于微生物学的核心ML概念.
- 适用于微生物学数据分析的ML任务概述.
- 在抗生素耐药性预测和微生物群与宿主疾病关联中使用ML的例子.
结论:
- 机器学习为推进微生物学研究提供了强大的工具.
- 一个基本的ML工具包使微生物学家能够利用这些技术.
- 机器学习的整合可以提高微生物学中的实验设计和翻译结果.
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