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

Area of Science:

• Microbiome research
• Computational biology
• Personalized medicine

Background:

• Treatment efficacy varies significantly among patients due to individual microbiome differences.
• Modulating the microbiome offers a potential strategy to enhance treatment outcomes.
• Identifying specific microbes that influence treatment success is crucial for microbiome-based therapies.

Purpose of the Study:

• To introduce a cloud platform, microbiome causal machine learning (MiCML), for analyzing microbiome data and treatment effects.
• To provide a user-friendly web environment for complex microbiome data analysis.

Main Methods:

• MiCML incorporates batch effect correction for large-scale microbiome datasets.
• Utilizes causal machine learning to estimate treatment effects and identify influential microbial taxa.
• Supports data from both randomized controlled trials and observational studies.

Main Results:

• MiCML enables the identification of microbes that enhance or inhibit primary treatment efficacy.
• The platform facilitates the analysis of treatment effects in relation to microbiome profiles.
• Batch effect correction addresses systematic variations in microbiome data.

Conclusions:

• MiCML serves as a valuable analytical tool for microbiome-based personalized medicine.
• The platform is accessible via a web server and a GitHub repository.
• MiCML aids in understanding the causal relationship between the microbiome and treatment outcomes.