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Redefining false discoveries in cancer data analyses

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The nature of biological networks still brings challenges related to computational complexity, interpretable results and statistical significance. Recent work proposes a new method that paves the way for addressing these issues when analyzing cancer genomic data.

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Fig. 1: Overview of FDRnet, proposed by Yang and colleagues.

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Author notes

  1. These authors contributed equally: Hanna Najgebauer, Umberto Perron.

Authors and Affiliations

  1. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK

    Hanna Najgebauer & Francesco Iorio

  2. Human Technopole, Milano, Italy

    Umberto Perron & Francesco Iorio

Authors
  1. Hanna Najgebauer
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  2. Umberto Perron
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  3. Francesco Iorio
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Corresponding author

Correspondence to Francesco Iorio.

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Competing interests

F.I. receives funding from Open Targets, a public–private initiative involving academia and industry and performs consultancy for the joint CRUK–AstraZeneca Functional Genomics Centre. All the other authors declare no competing interests.

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Najgebauer, H., Perron, U. & Iorio, F. Redefining false discoveries in cancer data analyses. Nat Comput Sci 1, 22–23 (2021). https://doi.org/10.1038/s43588-020-00008-5

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