Domenica D’Elia
STAFF
Project
ML4Microbiome
Statistical and machine learning techniques in human microbiome studies
Name
Statistical and machine learning techniques in human microbiome studies
Acronym
ML4Microbiome
Location
Bari
Start Date
2019
End Date
2023
Funder
COST (European Cooperation in Science and Technology), a funding organisation for research and innovation networks
Partners
Marcus Claesson (University College Cork, Ireland); Randi J. Bertelsen (University of Bergen, Norway); Dimitrios VLACHAKIS (Agricultural University of Athens, Greece); Tatjana Lončar-Turukalo (Faculty of Technical Sciences, University of Novi Sad, Serbia); Jaak Truu (University of Tartu, Estonia); Leo Lahti (University of Turku, Finland); Christian Jansen (Biome Diagnostics GmbH, Wien, Austria)
The ML4Microbiome COST Action (CA18131) aims to create productive symbiosis between discovery-oriented microbiome researchers and data-driven Machine Learning experts, through regular meetings, workshops and training courses. The main goal is first optimise and then standardise the use of statistical modelling and Machine Learning (ML) methods specifically tailored to the analysis of human microbiome data and the creation of publicly available benchmark datasets. Correct usage of these approaches will allow for better identification of predictive and discriminatory ‘microbiomics’ features for the identification of key player in intestinal and non-intestinal diseases, e.g. inflammatory bowel disease, diabetes and liver cirrhosis, along with brain development and behaviour, increase study repeatability, and provide mechanistic insights into possible causal or contributing roles of the microbiome in human diseases. This Action will also investigate automation opportunities and define priority areas for novel development of ML/Statistics methods targeting microbiome data.
