What we do

The Computational Genetics and Epigenetics of Cancer Group aims to assess contribution of chromatin changes to cancer initiation and progression and associate them with genetic modifications. To achieve this goal, we employ advanced data analytics (in particular machine learning algorithms) and get biological insights, which we can further validate in the lab. The team is actively involved in creating novel accurate methods for studying genetic and epigenetic changes in cancer. Check the whole list of softwares developed by the team.

Find out about the Group’s activities

Main publications 2021

  • Malenová G et al.
    Exploring pathway-based group lasso for cancer survival analysis: a special case of multi-task learning
    Frontiers in Genetics, 10.3389/fgene.2021.771301
  • Polit L et al.
    CHIPIN: ChIP-seq Inter-sample Normalization based on signal invariance across transcriptionally constant genes
    BMC Bioinformatics, 10.1186/s12859-021-04320-3
  • Kerdivel G & Boeva V
    Chromatin Immunoprecipitation Followed by Next-Generation Sequencing (ChIP-Seq) Analysis in Ewing Sarcoma
    Methods in Molecular Biology, Springer, 10.1007/978-1-0716-1020-6_21


eth zurich

Valentina Boeva
Computational Epigenetics of Cancer
ETH Zurich
Group Webpage

Domain(s) of activity:

  • Systems biology
  • Epigenetics
  • Gene regulatory network analysis
  • Gene regulation
  • Human genetics
  • Machine learning
  • Mathematical modelling
  • Next generation sequencing
  • Oncology
  • Personalised medicine
  • Signaling pathways

Domain(s) of application:

  • Medicine and health