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微生物原料:微生物学家的人工智能

Hanqun Cao1,2,3,4, Cesar de la Fuente-Nunez1,2,3,4

  • 1Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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概括
此摘要是机器生成的。

人工智能 (AI) 和机器学习 (ML) 为微生物学提供了强大的工具,有助于抗生素的发现和诊断. 这本小册子指导微生物学家在实际的ML应用,数据准备和负责任的解释上进行有影响力的研究.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.微生物学的微生物.

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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:

  • 人工智能 (AI) 和机器学习 (ML) 在科学研究中越来越有影响力.
  • 微生物学从AI/ML中受益,用于诸如抗生素发现,耐药性预测和诊断等任务.
  • 微生物学家需要一种实际的方法来有效地利用ML,而不需要从头开始构建算法.

研究的目的:

  • 为微生物学家提供一本关于在他们的领域应用机器学习 (ML) 的实用手册.
  • 引导研究人员选择适当的ML方法,准备数据,并负责任地解释结果.
  • 突出ML在抗生素发现,微生物组研究和病原体基因组学中的潜力.

主要方法:

  • 机器学习模型从复杂而往往杂的生物数据中学习模式.
  • 监督学习模型用于将序列特征映射到诸如抗菌素敏感性之类的结果.
  • 无监督学习方法有助于探索,聚类和缩小可视化的维度.

主要成果:

  • ML有助于识别和设计使用生成AI的新型抗生素化合物.
  • 在微生物组研究中,ML有助于揭示社区结构,并将微生物种类与表型联系起来.
  • ML可以从病原体基因组数据准确预测物种身份,抗菌素敏感性和MIC.

结论:

  • 在微生物学中有效的ML应用取决于良好的问题框架,数据预处理和实验验证.
  • 机器学习在各种微生物学领域提供了变革性的潜力,从药物发现到诊断.
  • 微生物学家可以通过了解其适当的使用和负责任的解释而不是算法开发来利用ML.