Didactic Elective Activity

Lectio Magistralis: Disease Trajectories – The Future of Humans as Model Organisms
Prof. Søren Brunak | University of Copenhagen

Date and Time: April 2, 17:00
Location: Aula Magna, Torre Biologica
Registration Link: Register Here

Speaker Bio

Søren Brunak, Ph.D., is a professor of Disease Systems Biology and Research Director at the Novo Nordisk Foundation Center for Protein Research, University of Copenhagen. His research integrates molecular systems biology with healthcare data analysis (electronic patient records, registries, and biobank questionnaires) to understand multimorbidities and identify treatment-related disease correlations. This approach facilitates patient stratification beyond genetic markers, incorporating clinical descriptions for precision medicine. Prof. Brunak is a member of the Royal Swedish Academy of Sciences (since 2016), the European Molecular Biology Organization (since 2009), and the Royal Danish Academy of Sciences and Letters (since 2004).

Abstract

As populations age, disease patterns become increasingly complex, with patients experiencing multiple illnesses throughout their lives. Concurrently, vast amounts of heterogeneous health data are being collected through electronic patient records, biobanks, and multi-omics cohort studies. This wealth of data supports a paradigm shift in biomedical research: considering humans as model organisms, allowing direct healthcare applications without reliance on animal models. The lecture will explore large-scale multimorbidity analysis from millions of patient records and discuss machine learning approaches to understand complex disease etiologies, advancing precision medicine.

Recommended Reading

• Network biology concepts in complex disease comorbidities
  Hu JX, Thomas CE, Brunak S. Nat Rev Genet. 2016;17(10):615-29. doi: 10.1038/nrg.2016.87.

• Disease trajectory browser for exploring temporal, population-wide disease progression patterns in 7.2 million Danish patients
  Siggaard T et al. Nat Commun. 2020;11(1):4952. doi: 10.1038/s41467-020-18682-4.

• Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models
  Allesøe RL et al. Nat Biotechnol. 2023;41(3):399-408. doi: 10.1038/s41587-022-01520-x.

• A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories
  Placido D (*) et al. Nat Med. 2023;29(5):1113-1123. doi: 10.1038/s41591-023-02289-2.