From Data to Discovery: Artificial Intelligence Boosts Biotechnology

Event Details
Date: 21.05.2026, 16:00 o'clock - 18:00 o'clock 
Location: N 2045, Universitätsstraße 6a, 86159 Augsburg
Organizer(s): Institut für Informatik
Topics: Studium, Wissenschaftliche Weiterbildung, Informatik, Gesundheit und Medizin
Series of events: Medical Information Sciences
Event Type: Vortragsreihe
Speaker(s): Prof. Dr. Prof. h.c. Andreas Dengel
BIOINF ASFDASDF DSFASF ASDF ASDF © University of Augsburg

In diesem Semester wird die im WiSe 2022/23 erfolgreich gestartete Vortragsreihe Medical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Donnerstag ab 16:00 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.


Biotechnology and drug research are facing increasingly complex challenges: diseases are becoming more individualized, drug development remains costly and slow, and the demand for sustainable solutions in medicine continues to grow. Generating new insights in this environment requires more than just data — it requires intelligent integration and interpretation. This talk presents a systematic AI-driven approach to understanding biological systems, based on a globally unique, deeply curated dataset that combines biological sequence data with rich semantic knowledge about entities and their relationships. By integrating large language models, knowledge graphs, and multi-modal data, we enable AI systems to uncover hidden biological patterns and generate actionable insights — often without extensive wet-lab experimentation. The lecture demonstrates how such data can be transformed into practical, explainable applications: from advanced sequence analysis and automated biomarker discovery to precise prediction of biological interactions and AI-supported drug repurposing and repositioning. The result is a new generation of AI tools that accelerates discovery, reduces costs, and supports more sustainable and personalized innovation in the life sciences.

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