Immunoinformatics and Machine Learning

Group Leader: Morten Nielsen

The main task of the immune system is to defend the host against pathogen infections. The immune system is in most cases highly effective, but if it fails, it can have dramatic implications both on individuals and society. Malaria, Tuberculosis, HIV, and cancer are some striking examples.

Understanding how the host immune system interacts with pathogens is essential to make sense of the successes and failures of the immune system, and can offer a crucial aid towards the development of therapeutic and prophylactic interventions against these diseases. A promising approach to address this complex problem has been to develop methods capable of predicting the behavior/function of key players of the immune system. The Immunoinformatics and Machine Learning group has played a central role in this field.

The core of the research within the Immunoinformatics and Machine Learning group deals with the development of novel and advanced data-driven prediction methods for pattern recognition in biological systems. The development of pattern recognition algorithms is pivotal for the construction of accurate prediction systems for receptor-ligand interactions in biological systems in general, and for our understanding of the response of the immune system to pathogens in particular.

The group has developed methods for the three main types of epitopes:

  1. B cell epitopes, which are the targets of antibodies and are used to recognize microorganisms outside cells
  2. Helper T lymphocyte (HTL) epitopes, which interact with CD4 T cells to activate cells that have taken up foreign substances
  3. Cytotoxic T lymphocyte (CTL) epitopes, which are the targets of CD8 T cells and are used to directly detect and kill infected cells.

Applying these methods, the Immunoinformatics and Machine Learning group is involved in a large number of collaborations focused on rational epitope and antigen discovery aiming at developing new vaccines and therapies for a wide range of diseases. They include diseases with major epidemiological significance such as HIV, Malaria and Tuberculosis, as well as potential bioterrorism agents such as influenza and pox.


Morten Nielsen
DTU Bioinformatics
+45 45 25 24 25
25 FEBRUARY 2017