Advisor: Karsten Borgwardt
The human genome contains a torrent of information that gives clues not only about human origin, evolution, biological function, but also diseases. The goal of my project aims at developing novel machine learning techniques to better understand the complex genomic data and also other forms of data that can represent patient diseases. Towards this goal, we may be able to reveal more about disease mechanisms and therapy outcomes, which therefore shed new lights on the findings for personalized medicine and healthcare for each patient.
- Including network models in the search for patient state representations
Host: University of Liege
November 2020 – January 2021Postponed due to COVID
- Deep Learning for patient state representation
Planned date: December 2021 – February 2022
- Member of the MLFPM team that participated in the EU vs Virus hackaton