Introduction to the topic
Our research group focuses on improving the understanding and treatment of women’s health conditions, including migraine and endometriosis. These prevalent yet under-researched diseases significantly impact quality of life.
We work at the intersection of clinical medicine and data science, combining hospital-based expertise with high-resolution molecular and physiological data. Our research spans large and diverse datasets, including patient-derived biospecimens, detailed symptom and wearable records, and multi-omic profiles (genomics, transcriptomics, proteomics, stereogenesis). By applying modern computational methods such as machine learning, time-series modelling, and network-based approaches, we aim to uncover disease mechanisms, identify biomarkers, and support the development of more individualized diagnostic and treatment strategies.
Project details
Menstrual cycle physiology is far more variable than traditionally assumed, yet little is known about how daily fluctuations in hormones, symptoms, and physiological signals interact across individuals. New data sources, including dense symptom tracking, serial hormone measurements, and wearable device outputs, make it possible to study the menstrual cycle with unprecedented resolution.
In this project, students will investigate patterns of cycle variability and explore how physiological signals (such as hormone levels, sleep, heart rate metrics, or symptom scores) change across different phases of the menstrual cycle. Students may examine relationships between hormone dynamics and symptom fluctuations, characterize distinct cycle patterns or phenotypes, or evaluate whether short-term changes in physiology can predict upcoming symptoms or cycle events.
The project provides hands-on experience in data preprocessing, longitudinal analysis, and visualization of high-frequency human health data. Students will work with real-world datasets from ongoing women’s health studies and contribute to research aimed at understanding individual variation in menstrual cycle biology and symptom experiences. They will collaborate with an interdisciplinary team and contribute to meaningful research aimed at advancing women’s health.
Selected relevant publications:
- Kogelman, L.J.A., Falkenberg, K., Ottosson, F. et al. (2023). Multi-omic analyses of triptan-treated migraine attacks gives insight into molecular mechanisms. Sci Rep 13, 12395. https://doi.org/10.1038/s41598-023-38904-1
- Chalmer, A.M., L.J.A. Kogelman, I. Callesen, et al. (2023) Sex differences in clinical characteristics of migraine and its burden: a population-based study. European Journal of Neurology, 30 (6). https://doi.org/10.1111/ene.15778
- L.J.A. Kogelman, A-L Esserlind, A.F. Christensen, et al. (2019) Migraine polygenic risk score associates with efficacy of migraine-specific drugs. Neurology Genetics 5 (6). https://doi.org/10.1212/NXG.0000000000000364
Recommended experience
Knowledge in molecular biology or genetics, as well as experience with bioinformatic tools/programming languages such as R and python.
Additional research application required
You will need to submit an additional research application through Student Registration in order to enroll in this course.
To submit your research application, you must already be admitted to DIS.
All research application materials must be submitted on the following dates by 23:59 Central European Time:
- November 1 for spring semester applicants
- May 1 for fall semester applicants
Complete your additional research application through Student Registration.
Syllabus
Fall 2025
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