The online Symposium “Grand Challenges of Data-Intensive Science in microbiome & metagenome data analysis and training” will be held on October 14th, 2021, from 12:00 to 16:30 CEST.

The Symposium is organised by ML4Microbiome in collaboration with EMBnet – The Global Bioinformatics Network and  GOBLET – The Global Organisation for Learning, Education and Training in Bioinformatics and the support of ELIXIR Italian Node and the Department of Computer Science of the University of Bari (IT). It is a joint event of the “EMBnet & GOBLET Annual General Meeting 2021”, which attract every year hundreds of researchers from all over the world to share and discuss issues related to Education & Training in Bioinformatics.

The focus of the Symposium will be on cutting-edge topics related to the analysis of microbiome data, emerging machine learning technologies and their application for the classification and standardization of sequencing data and associated metadata for the interpretation of their biological meaning. The aim is to give an overview of ML4Microbiome activities, work in progress and achievements to a large community of researchers working in the same or related fields to foster and increase ML4Microbiome collaborations in both research & training.

 

Confirmed Invited Speakers & Talks:

Fotis Psomopoulos (ELIXIR ML Focus Group) – The ELIXIR Machine Learning Focus Group: achievements and road ahead

Salvador Capella – ELIXIR ML Focus Group & ELIXIR Tools Platform Executive Committee (ExCo) – Lessons learnt in Benchmarking in the context of OpenEBench/ELIXIR Communities

Leo Lahti (ML4Microbiome member) – Open data science in microbiome research

Isabel Moreno (ML4Microbiome member) – ML4microbiome for the advance of precision medicine: what Biomedicine expects from u

Enrique Carrillo (ML4Microbiome member) – Machine Learning & Microbiome for Precision Nutrition

 

Please use the form available here to register for the Symposium and other associated events!

Registration is free of charge and open to everybody in the research community

 

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