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Recovering biological networks

Data meets prior knowledge for interpretable mechanistic inference in biology

Nature Machine Intelligence volume 7, pages 987–988 (2025)Cite this article

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A unified optimization framework, CORNETO, introduces a versatile approach to knowledge-driven biological network inference, bringing machine learning sensibilities to systems biology.

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Fig. 1: An example of a knowledge-driven biological network inference challenge in metabolic profiling for a single sample and multiple samples.

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Authors and Affiliations

  1. Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    David Gomez-Cabrero & Jesper N. Tegnér

  2. Unit of Computational Medicine, Center for Molecular Medicine, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden

    Jesper N. Tegnér

  3. Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Jesper N. Tegnér

  4. Science for Life Laboratory, Solna, Sweden

    Jesper N. Tegnér

Authors
  1. David Gomez-Cabrero
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  2. Jesper N. Tegnér
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Correspondence to David Gomez-Cabrero.

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The authors declare no competing interests.

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Gomez-Cabrero, D., Tegnér, J.N. Data meets prior knowledge for interpretable mechanistic inference in biology. Nat Mach Intell 7, 987–988 (2025). https://doi.org/10.1038/s42256-025-01075-x

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