Biological network analysis with statistics and machine learning


We kindly like to invite you to a presentation of Giulia Muzio, PhD candidate, from the Professur for Data-Mining, ETH Zürich, Department of Biosystems Science and Engineering (BSSE) on:

Biological network analysis with statistics and machine learning

Biological networks represent biological processes in form of a graph, allowing to model both the involved biological entities (i.e., the nodes) and their relationships (i.e., the edges). For example, the interactions between proteins are modeled by means of protein-protein interaction (PPI) networks, where the nodes correspond to the proteins and the edges define the presence of an interaction between the connected proteins. This graph representation is very convenient as it provides an intuitive representation of complex biological knowledge and, furthermore, it permits the use of graph theory, statistics, and machine learning tools to analyze and exploit such knowledge. In fact, biological networks can be utilized to enhance power and interpretability in genome-wide association studies or as a framework to integrate prior information in various prediction tasks, e.g., cancer immune subtype prediction and gene function prediction. In this talk, I will present different tools that incorporate biological network information and will show their applications on both simulated and real data, and across different tasks.

Date, time:                  Thursday, May 11, 2023 at 11.00 s.t.

followed by a joint lunch at the Hochgebirgsklinik

Location:                     Davos, Medizincampus, Room Seehorn

Registration:                Please, register until Wednesday, May 3, the latest by sending an email to

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