EU-COST (European Cooperation in Science and Technology) action; Statistical and machine learning techniques in human microbiome studies
We are part of an EU-COST and leading one the working group. This action 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 optimise and then standardise 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.
Leading the MSc Microbiome in Health & Disease
Dr. Shoaie co-leads the MSc Microbiome in Health & Disease. The MSc program provides students with a unique background in all aspects of both analysis of microbiome and determining the role of microbiome in pathology with experience in both computational and experimental techniques. The courses bring together teaching in systems biology and bioinformatics with molecular biology, microbiology, immunology and physiology.
Our lab lead on two computational modules:
Microbiome Analysis (45 credits)
Co-leader: Sunjae Lee
Systems & Synthetic Biology (30 credits)
Co-leader: Gholamreza Bidkhori