Advancing and Assessing Computational Methods in Systems Biomedicine

Event Details
Date: 07.11.2024, 17:30 o'clock - 19:00 o'clock 
Location: N2045, Universitätsstraße 2, 86159 Augsburg
Organizer(s): Lehrstuhl für Biomedizinische Informatik, Data Mining und Data Analytics
Topics: Studium, Wissenschaftliche Weiterbildung, Informatik, Gesundheit und Medizin
Series of events: Medical Information Sciences
Event Type: Vortragsreihe
Speaker(s): Dr. Clemens Kreutz
BIOINF ASFDASDF DSFASF ASDF ASDF © University of Augsburg

In diesem Wintersemester 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 17:30 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.


Due to the rapid progress in developing experimental techniques, establishing and improving analysis methods is one of the major challenges in computational life sciences. For many analysis tasks, however, the limitations and performance of competing methods remain unknown, and there are no clear rules or guidelines for selecting the optimal analysis method. Benchmark studies have proven to be valuable tools for evaluating the performance and applicability of analysis approaches. However, they are often subject to methodological limitations and deficiencies, leading to potential bias in the results.

In my presentation, I will give an overview of novel approaches developed in my group in the context of mathematical modeling and omics analyses. In particular, I will summarize our ongoing efforts to improve the methodology of benchmark studies. By generally incorporating rigorous planning, design, and analysis principles in benchmark studies, we aim to promote the development of novel analysis approaches and the identification of decision rules for optimal method selection in practice. Especially in view of the enormous efforts to apply deep learning methods in all areas of research, reliable performance comparisons are of great importance.

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