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/...
Saved in:
Main Authors: | Hyunwook Koh, Jihun Kim, Hyojung Jang |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2025-01-01
|
Series: | BioData Mining |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13040-025-00422-3 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Long COVID and gut microbiome: insights into pathogenesis and therapeutics
by: Raphaela I. Lau, et al.
Published: (2025-12-01) -
Human reference microbiome profiles of different body habitats in healthy individuals
by: Sujin Oh, et al.
Published: (2025-02-01) -
The microbiome of Total Suspended Particles and its influence on the respiratory microbiome of healthy office workers
by: Giulia Solazzo, et al.
Published: (2025-02-01) -
A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument
by: Nele Taba, et al.
Published: (2025-12-01) -
Tumor microbiome: roles in tumor initiation, progression, and therapy
by: Shengxin Zhang, et al.
Published: (2025-02-01)