Mario Sänger, a member of the group “Human-computer interaction for Scientific Software”, successfully defended his PhD thesis on November 25, 2024. His work focuses on using representation learning to extract meaningful connections between biomedical entities, such as genes, diseases, proteins, and pharmaceuticals from a corpus of PubMed abstracts, as well as biomedical knowledge bases. In addition to demonstrating the feasibility of this corpus-wide approach, he also benchmarked and tested existing pre-trained language models (PLMs) for sentence-level relation prediction. His results show that additional context from biomedical knowledge databases does not enhance the most robust carefully tuned PLMs.
In FONDA, he collaborated with Prof. Dr. Thomas Kosch, exploring the use of ChatGPT as a tool to support users in designing and implementing scientific workflows.
Congratulations Mario, and all the best!