利用机器学习进行元基因组数据分析:趋势和应用
Shradha Sharma1,2,3, Hari Priya Narahari3,4, Karthik Raman2,3
1Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology (IIT) Madras, Chennai, Tamil Nadu, India.
mSystems
|October 7, 2025
概括
机器学习 (ML) 为分析复杂的元基因组数据提供了强大的工具. 这篇评论探讨了ML的应用,挑战和微生物科学中的未来方向.
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:
- 大基因组测序提供了高分辨率的微生物概况,但产生了高维度,稀疏和噪音数据.
- 分析复杂的元基因组数据集带来了下游数据分析的重大挑战.
研究的目的:
- 审查当前机器学习 (ML) 在元基因组数据分析中的应用.
- 讨论挑战,模型解释性,并将ML与机械模型进行比较.
- 为微生物科学预览人工智能和机器学习的未来方向.
主要方法:
- 对监督和无监督学习,时间序列建模,转移学习,因果学习和生成模型的调查.
- 讨论模型可解释性和可解释AI (XAI).
- 对ML和机械模型进行比较分析.
主要成果:
- 机器学习提供了一个强大的工具包,可以从大型复杂的元基因组数据集中提取见解.
- 关键的挑战包括数据复杂性和对模型可解释性 (XAI) 的需求.
- ML和机械模型为微生物组研究提供了互补的优势.
结论:
- 人工智能和机器学习对于推进微生物科学至关重要.
- 未来的方向包括多omics集成,合成数据生成和Agentic AI.
- 机械学习和机械方法之间的协同作用将推动未来的发现.
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