机器学习和深度学习在微生物组研究中的应用
Ricardo Hernández Medina1, Svetlana Kutuzova1,2, Knud Nor Nielsen1,3
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark.
ISME communications
|November 8, 2023
概括
机器学习和人工智能正在通过分析复杂的微生物社区数据来彻底改变微生物组研究. 这些先进的方法有助于揭示微生物组组成和功能之间的复杂联系,用于各种应用.
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:
- 微生物组是影响人类健康,植物弹性和生物地化学循环的重要微生物生态系统.
- 了解微生物组的组成和功能对于各种科学学科至关重要.
- 机器学习 (ML) 和深度学习 (DL) 为微生物组数据分析提供了强大的工具.
研究的目的:
- 为如何在当代微生物组研究中使用人工智能 (AI) 方法提供概述.
- 突出微生物组数据 (组成,稀疏,高维) 所带来的独特挑战.
- 讨论微生物组研究的传统和新型ML/DL方法.
主要方法:
- 对微生物组研究中人工智能应用的当前文献的综述.
- 介绍针对微生物组数据量身定制的传统和先进的ML/DL技术.
- 讨论数据特征,需要专门的分析方法.
主要成果:
- 人工智能方法,特别是ML和DL,越来越多地用于分析复杂的微生物群数据集.
- 由于微生物组数据的组成性,稀疏性和高维度性质,需要专门的数据处理.
- 不同的ML/DL方法为阐明微生物组的组成-功能关系提供了明显的优势.
结论:
- 人工智能是微生物组研究的变革性工具,可以更深入地了解微生物社区动态.
- 应对数据挑战是最大限度地发挥ML/DL在微生物科学中的潜力的关键.
- 未来的方向包括完善人工智能管道,以克服当前的瓶并增强分析能力.
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