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Epigenetic signatures for complex diseases

Signatures for complex diseases using machine learning

The ultimate goal of the project is to identify molecular features (MFs) that can be predictive of a particular disease phenotype through machine learning solutions. Such an identification will contribute towards more accurate diagnosis or prognosis and may provide clinicians and caretakers the opportunity to provide a more personalized treatment in the future.

Participating organisations

Amsterdam University Medical Centers, location VU
Netherlands eScience Center
Life Sciences
Life Sciences

Team

Contact person

Sonja Georgievska

Sonja Georgievska

Senior eScience Research Engineer
Netherlands eScience Center
0000-0002-8094-4532Mail Sonja
PH
Peter Henneman
Jisk Attema
Programme Manager
Netherlands eScience Center
0000-0002-0948-1176
Cunliang Geng
eScience Research Engineer
Netherlands eScience Center
0000-0002-1409-8358
Sonja Georgievska
Senior eScience Research Engineer
Netherlands eScience Center
0000-0002-8094-4532
ALY
Andrew Yung Fong Li Yim
PhD student/Advisor
Amsterdam University Medical Centers, University of Amsterdam
0000-0002-0754-0953
AV
EvdL
Eva L. van der Linden