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Biological datasets

Decoding omics via representation learning

Nature Computational Science volume 6, pages 17–18 (2026)Cite this article

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A framework called AUTOENCODIX benchmarks diverse autoencoder architectures in biological molecular profiling data, enabling insights from complex, multi-layered data.

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Fig. 1: Overview of AUTOENCODIX.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, University of Calgary, Calgary, Alberta, Canada

    Dinghao Wang & Qingrun Zhang

  2. Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada

    Qingrun Zhang

  3. Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada

    Qingrun Zhang

  4. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada

    Qingrun Zhang

Authors
  1. Dinghao Wang
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  2. Qingrun Zhang
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Correspondence to Qingrun Zhang.

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The authors declare no competing interests.

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Wang, D., Zhang, Q. Decoding omics via representation learning. Nat Comput Sci 6, 17–18 (2026). https://doi.org/10.1038/s43588-025-00909-3

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