MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles.

IF 4 3区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biodata Mining Pub Date : 2025-01-30 DOI:10.1186/s13040-025-00422-3

Hyunwook Koh, Jihun Kim, Hyojung Jang

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引用次数: 0

Abstract

Background: 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.

Results: 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.

Conclusion: 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 ).

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来源期刊

Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

7.90

自引率

0.00%

发文量

审稿时长

23 weeks

期刊介绍： BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.