Machine Learning for Single-Cell Omics
Merel Kuijs (Ph.D. Student)
In spite of the massive effort in ensuring drug safety and efficacy, some drugs fail to achieve a therapeutic effect and others have severe side effects. A personalized approach to medicine is needed to maximize every patient’s chance of recovery and relieve pressure on healthcare systems, but data to inform precision medicine had long been lacking. Recently, single-cell sequencing technology has generated a wealth of new biological data, enabling biologists to study biological systems at unprecedented resolution. My work focuses on the development of methods to interrogate complex single-cell data. My goal is to turn this data into an understanding of the individual patterns that underlie health and disease, gaining actionable insights that will advance precision medicine.
|Primary Host:||Fabian Theis (Helmholtz Center Munich)|
|Exchange Host:||Karsten Borgwardt (ETH Zürich)|
|PhD Duration:||01 August 2021 - 31 July 2024|
|Exchange Duration:||- Ongoing - Ongoing|