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Embedding gene sets in low-dimensional space

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An important task in system biology is to understand cellular processes through the lens of gene sets and their expression patterns. Machine learning can help, but genes form complex interaction networks, and levarging this information in machine learning applications requires a sophisticated data representation.

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Fig. 1: One of the most common results of systems biology analysis is the identification of sets of related genes.

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Acknowledgements

The authors are supported by the Intramural Research Programs of the National Library of Medicine at National Institutes of Health, USA.

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  1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Jan Hoinka & Teresa M. Przytycka

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  1. Jan Hoinka
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  2. Teresa M. Przytycka
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Correspondence to Teresa M. Przytycka.

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The authors declare no competing interests.

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Hoinka, J., Przytycka, T.M. Embedding gene sets in low-dimensional space. Nat Mach Intell 2, 367–368 (2020). https://doi.org/10.1038/s42256-020-0204-3

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