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  • Perspective
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Machine learning in rare disease

Nature Methods volume 20, pages 803–814 (2023)Cite this article

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Abstract

High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.

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Fig. 1: Combining datasets to increase data for training ML models.
Fig. 2: Representation learning can extract useful features from high-dimensional data.
Fig. 3: Strategies to reduce misinterpretation of ML model output in rare disease.
Fig. 4: Application of KGs can improve ML in rare disease.
Fig. 5: Feature-representation-transfer approaches learn representations from a source domain and apply them to a target domain.
Fig. 6: Combining multiple strategies strengthens the performance of ML models in rare disease.

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Acknowledgements

This work was supported in part by Alex’s Lemonade Stand Foundation, the National Human Genome Research Institute (R01 HG010067), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01 HD109765), the Neurofibromatosis Therapeutic Acceleration Program, the Children’s Tumor Foundation and the Gilbert Family Foundation.

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Author notes

  1. These authors contributed equally: Jineta Banerjee, Jaclyn N. Taroni.

Authors and Affiliations

  1. Sage Bionetworks, Seattle, WA, USA

    Jineta Banerjee, Robert J. Allaway & Justin Guinney

  2. Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, Philadelphia, PA, USA

    Jaclyn N. Taroni & Deepashree Venkatesh Prasad

  3. Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA

    Casey Greene

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Contributions

Authorship was determined using ICMJE recommendations. Conceptualization: J.B., J.N.T., R.J.A., C.G., J.G; data curation: not applicable; formal analysis: not applicable; funding acquisition: R.J.A.; investigation: J.B., J.N.T., R.J.A.; methodology: J.B., J.N.T., R.J.A.; project administration: J.B.; resources: J.B., J.N.T., R.J.A.; software: not applicable; supervision: J.B., C.G.; validation: not applicable; visualization: D.V.P.; writing (original draft): J.B., J.N.T., R.J.A.; writing (review and editing): J.B., J.N.T., R.J.A., C.G., J.G.

Corresponding author

Correspondence to Casey Greene.

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Competing interests

J.G. is currently employed at Tempus Labs, a precision medicine company. J.N.T. and D.V.P. are employed with Alex’s Lemonade Stand Foundation, a research funder. The remaining authors declare no competing interests.

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Nature Methods thanks Pejman Mohammadi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Banerjee, J., Taroni, J.N., Allaway, R.J. et al. Machine learning in rare disease. Nat Methods 20, 803–814 (2023). https://doi.org/10.1038/s41592-023-01886-z

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