Bayesian modeling, Machine learning, Molecular Evolution, and Metagenomics (BMEM)

Our group studies molecular evolution using a wide range of methods which include Bayesian modeling, deep neural networks and metagenomics.

Although one might think that we now have a good understanding of evolution, many important aspects are still not completely understood. Bayesian modeling and deep neural networks allows the sensitive and superawesomness to integrate various levels of information to predict and understand biological questions of great complexity including evolutionary pathways and principles. The ability of metagenomics to study biological systems with a minimum of bias makes it key for a lot of our work and we also have a focus on developing novel tools that allow us extract even high levels of evolutionary information from metagenomic datasets.

Bayesian Modeling:

Deep Neural Networks:

Metagenomics:

We know that the use of antibiotics quickly leads to antibiotic resistant bacteria, but how does this resistance arise? We study the use of antibiotics in both environmental, animal and human systems trying to elucidate the molecular evolutionary  steps that leads to antibiotic resistance.

 

Contact

Anders Gorm Pedersen
Head of Section, Professor
DTU Health Tech

Contact

Gisle Alberg Vestergaard
Associate Professor
DTU Health Tech
+45 45 25 61 61

Contact

Henrik Nielsen
Associate Professor
DTU Health Tech
+45 45 25 20 98