During his work, Johan Lassen developed methods for improving the accuracy of metabolomics, which is the analysis of small molecules in biological samples. Using modern statistical and machine learning techniques, he addressed challenges related to data variability. He achieved significant results, including estimating people's biological ages, modeling osteoporosis signals one year before diagnosis, and predicting antibiotic resistance.

The presented advancements in metabolomics data analysis could lead to better understanding and diagnosis of diseases.

The PhD degree was completed at the Bioinformatics Research Centre, Faculty of Natural Sciences, Aarhus University.

This summary was prepared by the PhD student.

Time: 28 February 2025 14:00
Place: Auditorium D1 1531-113, Ny Munkegade 116 , 8000 Aarhus C
Title of dissertation:  Addressing sources of variance in large-scale metabolomics
Contact information: Johan K. Lassen, e-mail: johan.lassen@birc.au.dk
Members of the assessment committee:
Associate Professor Marie Mardal, Department of Forensic Medicine, University of Copenhagen
Professor Henrik Green, Department of Biomedical and clinical sciences, Linköping University, Sweden
Associate Professor Kasper Munch, Department of Molecular Biology and Genetics, Aarhus University (chair)
Main supervisor:
Associate Professor Palle Villesen, BiRC, Aarhus University, Denmark
Co-supervisor:
Associate Professor Kirstine Lykke Nielsen, Department of forensic medicine, Aarhus University, Denmark
Language: The PhD dissertation will be defended in English
The defence is public.

The PhD thesis is available for reading at the Graduate School of Natural Sciences/GSNS

Raw metabolomics data

The landscape of peaks represents our metabolic image. An image that reveals our well-being and status, much like a portrait but more descriptive. Each peak represents a metabolite. By reorganizing the data, we can find all metabolite concentrations and describe their roles in diseases.