Research subject Machine learning in bioinformatics

Combining large scale life-science data with artificial intelligence is crucial for the continued progression of our understanding of the molecular processes that govern life. We propose developing novel deep-learning methods to provide an unprecedented accurate description of the human proteome.

We are only starting to understand the functional networks of the proteins encoded in the now sequenced human genome. The group is addressing this issue using computational approaches, such as hidden Markov models, evolutionary models, and discriminant statistical methods - aiming to predict function, both with regards to biochemical activity and pathways.

Membrane proteins serve a number of very important functions in both prokaryotic and eukaryotic cells. They are built according to structural principles different from those of globular proteins.