MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles
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/...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BioData Mining |
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| Online Access: | https://doi.org/10.1186/s13040-025-00422-3 |
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| author | Hyunwook Koh Jihun Kim Hyojung Jang |
| author_facet | Hyunwook Koh Jihun Kim Hyojung Jang |
| author_sort | Hyunwook Koh |
| collection | DOAJ |
| description | 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 ). |
| format | Article |
| id | doaj-art-eda067e2eb78465490a618c41a8df8ca |
| institution | Kabale University |
| issn | 1756-0381 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BioData Mining |
| spelling | doaj-art-eda067e2eb78465490a618c41a8df8ca2025-02-02T12:11:39ZengBMCBioData Mining1756-03812025-01-0118111810.1186/s13040-025-00422-3MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profilesHyunwook Koh0Jihun Kim1Hyojung Jang2Department of Applied Mathematics and Statistics, The State University of New York, KoreaDepartment of Applied Mathematics and Statistics, The State University of New York, KoreaDepartment of Applied Mathematics and Statistics, The State University of New York, KoreaAbstract 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 ).https://doi.org/10.1186/s13040-025-00422-3Human microbiomeCausal machine learningMicrobiome-based therapeuticsMicrobiome-based drug developmentMicrobiome-based diagnosticsMicrobiome-based personalized medicine |
| spellingShingle | Hyunwook Koh Jihun Kim Hyojung Jang MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles BioData Mining Human microbiome Causal machine learning Microbiome-based therapeutics Microbiome-based drug development Microbiome-based diagnostics Microbiome-based personalized medicine |
| title | MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles |
| title_full | MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles |
| title_fullStr | MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles |
| title_full_unstemmed | MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles |
| title_short | MiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles |
| title_sort | micml a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles |
| topic | Human microbiome Causal machine learning Microbiome-based therapeutics Microbiome-based drug development Microbiome-based diagnostics Microbiome-based personalized medicine |
| url | https://doi.org/10.1186/s13040-025-00422-3 |
| work_keys_str_mv | AT hyunwookkoh micmlacausalmachinelearningcloudplatformfortheanalysisoftreatmenteffectsusingmicrobiomeprofiles AT jihunkim micmlacausalmachinelearningcloudplatformfortheanalysisoftreatmenteffectsusingmicrobiomeprofiles AT hyojungjang micmlacausalmachinelearningcloudplatformfortheanalysisoftreatmenteffectsusingmicrobiomeprofiles |