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Computational biology

Mutual ties

Nature Reviews Cancer volume 10, pages 82–83 (2010)Cite this article

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The algorithms designed by Goodarzi et al. are based on the concept of mutual information — a method derived from information theory that quantifies dependencies between observations. First, the authors identify deregulated pathways by calculating the mutual information between known pathways and a given cancer gene expression profile. Second, by computing the mutual information between the presence of sequence motifs in gene regulatory regions and the expression of corresponding genes, cis-regulatory elements that best explain the expression profile are uncovered. Last, by associating the independently identified pathways and regulatory elements, the authors are able to globally map candidate regulatory motifs that underlie key pathway perturbations.

the computational algorithms developed by Tavazoie and colleagues could be a valuable tool in discovering pathways perturbed in specific tumours

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ORIGINAL RESEARCH PAPER

  1. Goodarzi, H. et al. Revealing global regulatory perturbations across human cancers. Mol. Cell 36, 900–911 (2009)

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  1. Sophie Atkinson
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Atkinson, S. Mutual ties. Nat Rev Cancer 10, 82–83 (2010). https://doi.org/10.1038/nrc2798

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  • DOI: https://doi.org/10.1038/nrc2798

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