Institute Seminar | Davide Cirillo | 2022-06-21 | 2/2 Discussion
Channel: CASUS.science
Date: 13/12/2022

TITLE: Graph-based approaches in biomedicine: from single cell transcriptomics to epidemiology by Davide Cirillo, Barcelona Supercomputing Center (BSC), Spain

Davide is the head of the Machine Learning for Biomedical Research Unit and Juan de la Cierva Incorporación fellow at BSC Computational Biology group. His area of expertise is data analysis and predictive modeling for Precision Medicine using network biology and machine learning. His research in precision medicine includes rare diseases and pediatric cancers, as well as ethics of Artificial Intelligence.

ABSTRACT: Relational associations that characterize biological systems, from cells to populations, can conveniently be represented in the form of complex graphs. In particular, graph structures with multiple types of connections, such as multilayer networks, have recently become one of the most important directions in network medicine. These representations man effectively express clinical and molecular information allowing mining properties and underlying features through a wide array of dedicated approaches, including topological data analysis. Artificial intelligence (AI) is playing a key role in this area. Indeed, thanks to the graphs’ ability to abstract and generalize structures, graph-based AI approaches, such as graph neural networks, are paving the way to the development of new ways to learn about bio-entities and their associations, enabling relational reasoning and combinatorial generalization. Lastly, to efficiently solve such large-scale graph problems, it is crucial to design High Performance Computing solutions that support extensive parallelism and scalability.

LICENSE:
CC BY-SA 4.0

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