Friday 26th November 2021  16.00-17.00 CET

Dr Bertrand De Meulder, PhD

Senior Researcher Ass. EISBM

Association European Institute for Systems Biology and Medicine, Vourles, Francem

Multiple omics clustering, data interpretation and predictive modelling

Abstract:

Patient stratification based on omics data is a relatively new area of research and application in health research, with many methods and standards being developed in the previous years. In this presentation, I will mention a few of those techniques (multi-omics clustering, Topological Data Analysis, data interpretation and predictive modelling) and illustrate their use in a real-life example.

Key references:

De Meulder et al. (2018) A computational framework for complex disease stratification form multiple large-scale datasets, BMC Syst. Biol., 12:60

Lefaudeux D*, De Meulder B* et al. U-BIOPRED clinical adult asthma clusters linked to a subset of sputum –omics. Journal of Allergy and Clinical Immunology, 2016.

Biography:

Bertrand De Meulder received a License in Biological Sciences in 2005 from the University of Namur (Belgium), a Master of Sciences in Bioinformatics and modelisation in 2007 from the Free University of Brussels (Belgium) and a Doctoral degree in 2013 in bioinformatics and cancer biology from the University of Namur. He then started working as a post-doctoral fellow at the European Institute of Systems Biology and Medicine (then a CNRS-ENS-UCBL laboratory), joining the U-BIOPRED European research project on severe asthma. He furthered his expertise in big data analytics and patient stratification, applying various techniques on multiple data modalities to identify subgroups of patients showing different underlying biology. He then worked as a researcher fellow for the eTRIKS European project, working on providing tools, standards, and platforms for big data analysis in health. He then joined the PIONEER research project on big data analysis on prostate cancer, as a senior researcher and WP leader, where he applied his skills to characterize, better understand and treat prostate cancer patients.

 

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