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Machine Learning and Causal Inference to Optimize Genomic Interventions using Disease State Representations

Bowen Fan (Ph.D. Student)

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.

Primary Host: Karsten Borgwardt (ETH Zürich)
Exchange Host: Kristel Van Steen (University of Liège & KU Leuven)
PhD Duration: 01 March 2020 - 31 December 2022
Exchange Duration: 01 November 2020 - 31 January 2021