ML4Microbiome- COST Action CA18131 Statistical and machine learning techniques in human microbiome studies

Acronym: ML4Microbiome

Implementation period: 22/02/2019 - 21/02/2023

GA number: CA18131 

Type of Project: COST

Internet presentation:


Project aim: This COST Action is committed to improving scientific understanding of the human microbiome data through facilitating its analysis using statistical and machine learning techniques, and to improving collaboration between microbiome researchers and data-driven experts.


About the project: In recent years, the human microbiome has been characterized in great detail in several large-scale studies as a key player in intestinal and non-intestinal diseases, e.g. inflammatory bowel disease, diabetes and liver cirrhosis, along with brain development and behavior. As more associations between microbiome and phenotypes are elucidated, research focus is now shifting towards causality and clinical use for diagnostics, prognostics, and therapeutics, where some promising applications have recently been showcased.
Microbiome data are inherently convoluted, noisy, and highly variable, and non-standard analytical methodologies are therefore required to unlock its clinical and scientific potential. While a range of statistical modelling and Machine Learning (ML) methods are now available, sub-optimal implementation often leads to errors, over-fitting, and misleading results, due to a lack of good analytical practices and ML expertise in the microbiome community.


Thus, this COST Action network will create productive symbiosis between discovery-oriented microbiome researchers and data-driven ML experts, through regular meetings, workshops and training courses. Together, it will first optimize and then standardize the use of said techniques, following the creation of publicly available benchmark datasets. Correct usage of these approaches will allow for better identification of predictive and discriminatory ‘omics’ features, increase study repeatability, and provide mechanistic insights into possible causal or contributing roles of the microbiome.

This Action will also investigate automation opportunities and define priority areas for novel development of ML/Statistics methods targeting microbiome data. Thus, this COST Action will open novel and exciting avenues within the fields of both ML/Statistics and microbiome research.


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