Graph-neural networks in the life sciences and beyond
Katharina Baum, Pauline Hiort, Pascal Iversen
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Complex data can often be naturally modeled as a graph. Graphs or networks describe the interaction between objects and are an effective tool to represent systems in many applications. Graph neural ... read more
Complex data can often be naturally modeled as a graph. Graphs or networks describe the interaction between objects and are an effective tool to represent systems in many applications. Graph neural networks are neural networks that directly input graphs and have recently emerged as a powerful tool to analyze networks and to predict properties of nodes and connections.
This seminar offers an in-depth exploration of Graph Neural Networks (GNNs) and their applications across various domains, with a particular emphasis on the life sciences and biomedicine. We will begin by discussing the fundamental concepts and architectures of GNNs, including graph convolutional networks (GCNs) and graph attention networks (GATs). Applications that are discussed include protein-protein interaction networks, drug discovery and personalized medicine. Students will read and present research papers and participate in critical discussions.
The language of this seminar is planned to be English. The students are encouraged to present and discuss in English, but contributions in German are also possible.
