Precision medicine
Our aim is to develop algorithms and tools to improve treatment and prognosis of cancer and brain disorders, utilizing large-scale data from genotyping, sequencing, multi-omics and neuroimaging, as well as contributing to implement computational infrastructure to facilitate uptake of this approach.
Comparative genomics
One approach under development is the use of graph-based representations of sequences that opens up for representing large- and small-scale variations across different species in a common reference structure, enabling standardized and consistent statistical inference on a scale that is not possible through the use of linear reference genomes.
Genome organization & dynamics
Our aim is to understand the interplay of various epigenetic modifications of DNA and histones, of chromatin structure and spatial genome organization, and of binding of proteins to DNA.
Microbiome bioinformatics
A large number of microorganisms are present in our bodies and in our environment and may have a profound impact on our health. We develop computational methods to analyse microbiome sequencing data in order to determine their composition, evolution, function, and association with disease.
Machine learning in biomedicine
We develop and apply machine learning methods to answer crucial questions in biomedical research : to characterize genome organization, to decipher adaptive immunity, for cancer biomarker discovery, for integrative modeling of multiple modalities of biomedical datasets including genetic, epigenetic, transcriptomic and imaging datasets. Our current methodological focus is on incorporating causal modeling and informative priors in deep learning models and multiple instance learning.