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Neutral Benchmarking in bioinformatics
If methodological research in bioinformatics can be summarized in a single sentence, it is this: „Our new method outperformed the existing ones“. But is it realistic to expect that every new method outperforms existing ones? Do such claims meaningfully help readers and are they trustworthy? What do bioinformaticians need to identify the most appropriate method for their application? In this talk, I will discuss the importance of neutral method comparison studies as a cornerstone for generating reliable evidence on the performance of methods and providing sound, practical guidance for method selection. Special emphasis will be placed on the design of such studies, the various sources of bias that may affect their results, and the incentive structure in science.
About the speaker
Anne-Laure Boulesteix is a professor at the Department of Medical Informatics, Biometry and Epidemiology of the Faculty of Medicine at LMU Munich. She is working at the interface between biostatistics, machine learning and medicine with a particular focus on metascience and evaluation of methods. She is a steering committee member of the STRATOS initiative, founding member of the LMU Open Science Center and president of the German Region of the International Biometric Society.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
