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  • Perspective
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Graph representation learning in biomedicine and healthcare

Nature Biomedical Engineering volume 6, pages 1353–1369 (2022)Cite this article

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Abstract

Networks—or graphs—are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example, molecular interactions, signalling pathways, disease co-morbidities or healthcare systems. In this Perspective, we posit that representation learning can realize principles of network medicine, discuss successes and current limitations of the use of representation learning on graphs in biomedicine and healthcare, and outline algorithmic strategies that leverage the topology of graphs to embed them into compact vectorial spaces. We argue that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines.

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Fig. 1: Representation learning for networks in biomedicine and healthcare.
Fig. 2: Algorithmic paradigms in graph representation learning.
Fig. 3: Biomedical applications of graph representation learning.
Fig. 4: Representation learning in four areas of biomedicine and healthcare.

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Acknowledgements

We gratefully acknowledge the support of the National Science Foundation, under grants IIS-2030459 and IIS-2033384, the US Air Force Contract No. FA8702-15-D-0001, the Harvard Data Science Initiative, and awards from Amazon Research, Bayer Early Excellence in Science, AstraZeneca Research, and Roche Alliance with Distinguished Scientists. M.M.L. is supported by T32HG002295 from the National Human Genome Research Institute and a National Science Foundation Graduate Research Fellowship. Any opinions, findings, conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the funders.

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  1. Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA

    Michelle M. Li

  2. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Michelle M. Li & Marinka Zitnik

  3. Health Data Science Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Kexin Huang

  4. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Marinka Zitnik

  5. Harvard Data Science Initiative, Cambridge, MA, USA

    Marinka Zitnik

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M.M.L. and M.Z. conceived the work and shaped its framing. M.M.L. performed background research and wrote the manuscript together with K.H. and M.Z. All authors discussed the content, and reviewed and edited the manuscript.

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Li, M.M., Huang, K. & Zitnik, M. Graph representation learning in biomedicine and healthcare. Nat. Biomed. Eng 6, 1353–1369 (2022). https://doi.org/10.1038/s41551-022-00942-x

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