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Technologies for doctors to make better decisions faster

Leveraging theory, computation and experiments, my group and I are creating novel measurement and prediction systems for integrated cancer biology. We work in translational projects on three frontiers:

cancer genome cancer tissue network biology
Tumour evolution Tissue context Interaction networks

(1) Revealing the mutational processes acting on cancer genomes, measuring their impact on patient phenotypes, and using them for personalised therapy decisions;

(2) Improved patient stratification, early detection, and prognosis by predictive modelling of tumour imaging and genomics data;

(3) Predicting strategies to overcome resistance and reduce toxicity by comparative network analysis of transcriptional responses to combinatorial CRISPR perturbations in single cells.



  • Drews et al (2022), A pan-cancer compendium of chromosomal instability, Nature
  • Gehrung et al (2021), Triage-driven diagnosis of Barrett esophagus for early detection of esophageal adenocarcinoma using deep learning, Nature Medicine
  • Crispin-Ortuzar et al (2020), Three-Dimensional Printed Molds for image-guided surgical biopsies: an open source computational platform, JCO CCI
  • Cmero et al (2020), SVclone: inferring structural variant cancer cell fraction, Nature Comms

... more publications


  • Drews2022_CIN_Compendium - central code hub for "A pan-cancer compendium of chromosomal instability", Drews et al. (2022).
  • SliDL - A Python library for deep learning on whole-slide images
  • OncoNEM - Clonal evolution trees from single cell data
  • MEDICC - intra-tumor copy-number comparisons
  • nem - inferring Nested Effects Models from downstream perturbation effects

... more software


Florian Markowetz
University of Cambridge CRUK Cambridge Institute
Robinson Way
Cambridge, CB2 0RE, UK
p: +44 (0) 1223 769 628
f: +44 (0) 1223 769 510 How to find us