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Updated: Oct 9, 2025

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Identifying features of genome evolution to exploit cancer vulnerabilities.

Rohan Dandage1, Christian R Landry1

  • 1Département de biologie, Université Laval, 1030 Avenue de la médecine, Québec, QC G1V 0A6, Canada; Département de biochimie, microbiologie et bio-informatique, Université Laval, 1030 Avenue de la médecine, Québec, QC G1V 0A6, Canada; Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, 1030 Avenue de la médecine, Québec, QC G1V 0A6, Canada; The Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université Laval, 1030 Avenue de la médecine, Québec, QC G1V 0A6, Canada; Centre de recherche en données massive (CRDM), Université Laval, 1030 Avenue de la médecine, Québec, QC G1V 0A6, Canada.

Cell Systems
|December 16, 2021
PubMed
Summary

Researchers developed a machine learning method to identify synthetic lethal (SL) paralogs for cancer therapy. This approach uses genome evolution patterns to predict robust SL interactions, advancing precision medicine strategies.

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