CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

Journal: Bioinformatics (Oxford, England)
PMID: 38940173

Abstract

MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate drug combinations and CRISPR-interference, has established them as an invaluable resource for the development of novel therapeutic approaches. Nevertheless, the combinatorial complexity of potential interventions makes a comprehensive exploration intractable. Hence, prioritizing interventions for further experimental investigation becomes of utmost importance.

Authors

  • Stefan Schrod
    Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany.
  • Helena U Zacharias
    Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany.
  • Tim Beissbarth
    3 Department of Medical Statistics, University Medical Center Goettingen, Goettingen, Germany.
  • Anne-Christin Hauschild
    IBM Life Sciences Discovery Centre, Princess Margaret Cancer Centre, Toronto, ON, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada; University Health Network, Toronto, ON, Canada.
  • Michael Altenbuchinger
    Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany.

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