Causal discovery for the microbiome.
Causal discovery for the microbiome.
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DOI：
10.1016/s2666-5247(22)00186-0
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发表时间：
2022-11
期刊：
影响因子：
38.2
通讯作者：
Pensar, Johan
中科院分区：
文献类型：
--
作者：
Corander, Jukka;Hanage, William P.;Pensar, Johan
微生物组的测量和操作通常被认为在了解人类复杂疾病的原因、开发新的治疗方法和找到预防措施方面具有巨大的潜力。许多研究已经发现微生物组和各种疾病之间有重要的联系；然而，科赫的经典假设提醒我们，在考虑微生物和疾病表现之间的关系时，因果推理的重要性。尽管观测微生物组数据中的因果发现面临许多挑战，但因果结构学习的方法学进步提高了大规模生物系统中因果效应的数据驱动预测的潜力。在这个个人观点中，我们展示了现有方法从元基因组数据推断因果效应的能力，并强调了引入比现有结构更灵活的因果结构为因果推理提供新机会的方法。我们的观察表明，微生物组研究可以进一步受益于过去5年在因果发现方面开发的工具，并从它们在其他地方的应用中学习。
Measurement and manipulation of the microbiome is generally considered to have great potential for understanding the causes of complex diseases in humans, developing new therapies, and finding preventive measures. Many studies have found significant associations between the microbiome and various diseases; however, Koch’s classical postulates remind us about the importance of causative reasoning when considering the relationship between microbes and a disease manifestation. Although causal discovery in observational microbiome data faces many challenges, methodological advances in causal structure learning have improved the potential of data-driven prediction of causal effects in large-scale biological systems. In this Personal View, we show the capability of existing methods for inferring causal effects from metagenomic data, and we highlight ways in which the introduction of causal structures that are more flexible than existing structures offers new opportunities for causal reasoning. Our observations suggest that microbiome research can further benefit from tools developed in the past 5 years in causal discovery and learn from their applications elsewhere.