Koh Hyunwook, Kim Jihun, Jang Hyojung
Department of Applied Mathematics and Statistics, The State University of New York, Korea, Incheon, South Korea.
BioData Min. 2025 Jan 30;18(1):10. doi: 10.1186/s13040-025-00422-3.
The treatment effects are heterogenous across patients due to the differences in their microbiomes, which in turn implies that we can enhance the treatment effect by manipulating the patient's microbiome profile. Then, the coadministration of microbiome-based dietary supplements/therapeutics along with the primary treatment has been the subject of intensive investigation. However, for this, we first need to comprehend which microbes help (or prevent) the treatment to cure the patient's disease.
In this paper, we introduce a cloud platform, named microbiome causal machine learning (MiCML), for the analysis of treatment effects using microbiome profiles on user-friendly web environments. MiCML is in particular unique with the up-to-date features of (i) batch effect correction to mitigate systematic variation in collective large-scale microbiome data due to the differences in their underlying batches, and (ii) causal machine learning to estimate treatment effects with consistency and then discern microbial taxa that enhance (or lower) the efficacy of the primary treatment. We also stress that MiCML can handle the data from either randomized controlled trials or observational studies.
We describe MiCML as a useful analytic tool for microbiome-based personalized medicine. MiCML is freely available on our web server ( http://micml.micloud.kr ). MiCML can also be implemented locally on the user's computer through our GitHub repository ( https://github.com/hk1785/micml ).
由于患者微生物群存在差异，治疗效果在不同患者中具有异质性，这反过来意味着我们可以通过操纵患者的微生物群特征来提高治疗效果。因此，将基于微生物群的膳食补充剂/治疗方法与主要治疗联合使用一直是深入研究的课题。然而，要做到这一点，我们首先需要了解哪些微生物有助于（或预防）治疗以治愈患者的疾病。
在本文中，我们介绍了一个名为微生物群因果机器学习（MiCML）的云平台，用于在用户友好的网络环境中使用微生物群特征分析治疗效果。MiCML尤其独特之处在于具有以下最新功能：（i）批次效应校正，以减轻由于底层批次差异导致的大规模微生物群数据中的系统变异；（ii）因果机器学习，以一致地估计治疗效果，然后识别增强（或降低）主要治疗效果的微生物分类群。我们还强调，MiCML可以处理来自随机对照试验或观察性研究的数据。
我们将MiCML描述为基于微生物群的个性化医学的有用分析工具。MiCML可在我们的网络服务器（http://micml.micloud.kr）上免费获取。MiCML也可以通过我们的GitHub仓库（https://github.com/hk1785/micml）在用户计算机上本地实现。
