Linguistics-driven machine learning to decipher the molecular language of immunity (ImmunoLingo)

The convergence environment wants to decipher how disease and antigen recognition is encoded in the immune system and to perform experiments in silico to improve intervention and treatment of human diseases.

Project leader: Victor Greiff, Associate Professor,Department of Immunology, Faculty of Medicine, UIO

Principal investigators:


The convergence environment will set out to decipher the molecular language of adaptive immunity, which we call ImmunoLingo.

Understanding Immunolingo is of incredible importance for the design and discovery of precision immunodiagnostics and immunotherapeutics.

Our goal will be achieved by transdisciplinarily combining expertise of life sciences, machine learning, statistics and linguistics researchers.

Primary objectives

To decipher how immunological information is encoded into the adaptive immune system.

Secondary objects

  • Shape the field of immunolinguistics by transdisciplinary interaction between traditional life sciences, computer scientists and social scientists
  • Train 2 PhD students and 2 postdocs (plus others) to a scientific career in systems immunology, computational immunology, biological statistics and biology-driven linguistics
  • Compare grammar of ImmunoLingo with that of natural languages
  • Develop machine learning methods for immune repertoire analysis
  • Create knowledge framework for immune-repertoire-based data protection
Published Mar. 26, 2019 10:35 AM - Last modified Mar. 26, 2019 3:09 PM