Machine Learning for Biological Network Analysis
Giulia Muzio (Ph.D. Student)
The main objective of my project is to develop methods for network-based genome-wide association studies (GWAS) that combine computational efficiency, statistical power and interpretability, thereby enabling the discovery of biological pathways underlying complex phenotypic traits. GWAS aim to identify statistical associations among genetic variants, also referred to as single nucleotide polymorphisms (SNPs), and disease risk or other phenotypes. The identification of such associations can positively affect healthcare as it enables enhanced disease prevention, diagnosis and personalised treatment. To date, GWAS rarely make use of the rich knowledge regarding biological networks, such as protein-protein interaction or gene regulation networks, underlying the phenomenon of interest. Including such contextual and functional information, however, can help to increase the statistical power as well as interpretability in GWAS aimed at complex biological traits that do not follow Mendelian inheritance laws and are influenced by environmental factors.
|Primary Host:||Karsten Borgwardt (ETH Zürich)|
|Exchange Host:||Volker Tresp (LMU Munich & Siemens)|
|PhD Duration:||01 September 2019 - 31 August 2022|
|Exchange Duration:||01 February 2021 - 30 April 2021|