What we do

In the Computational Cancer Biology Group, our aim is to study the interactions between cancer and immune cells. To this end, we develop machine learning algorithms to analyse large-scale genomics and proteomics data. In particular, we are focusing on molecular and cellular aspects of cancer immune cell interactions. At the molecular level, we develop tools to predict (neo-)antigen presentation by integrating large HLA peptidomics datasets. At the cellular level, we are developing novel approaches to characterizing immune infiltrations and the different states of immune cells from gene expression profiles of tumours and immune cells.
All our tools are available at https://github.com/GfellerLab

Highlights 2017

  • Novel bioinformatics tools to analyze large HLA peptidomics datasets (MixMHCp) and improved HLA-I ligand predictor (MixMHCpred).
  • First identification of NK cells in non-mammalian species.
  • Estimation of the Proportion of Immune and Cancer cells (EPIC) from bulk tumor gene expression data.

Find out more about the Group’s activities

Main publications



David Gfeller
Computational Cancer Biology Group
University of Lausanne
Group Webpage

Domains of activity:

  • Genes and genomes
  • Immunology
  • Machine learning
  • Peptidomics
  • Single-cell biology
  • Transcriptomics

Domains of application:

  • Medicine and health
  • Basic research