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Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics

Nature Machine Intelligence volume 6, pages 15–24 (2024)Cite this article

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Abstract

Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to learn generalizable and clinically applicable diagnostics. Here we argue that a causal perspective improves the identification of these challenges and formalizes their relation to the robustness and generalization of machine learning-based diagnostics. To make for a concrete discussion, we focus on a specific, recently established high-dimensional biomarker—adaptive immune receptor repertoires (AIRRs). Through simulations, we illustrate how major biological and experimental factors of the AIRR domain may influence the learned biomarkers. In conclusion, we argue that causal modelling improves machine learning-based biomarker robustness by identifying stable relations between variables and guiding the adjustment of the relations and variables that vary between populations.

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Fig. 1: Developing an AIRR-based diagnostic.
Fig. 2: Examples of selection bias in causal models.
Fig. 3: The different causal roles HLA can take for different types of immune-related disease.
Fig. 4: Experiments showing immune-state prediction performance under different causal models.
Fig. 5: Batch effects may lead to higher error rates in transcriptomic and AIR settings and might result in classifiers learning spurious signals, especially in the AIR setting.

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Data availability

All data and results for the analysis presented in the manuscript are openly available on Zenodo at https://zenodo.org/record/7756163 (experiment 1), https://zenodo.org/record/7752837 (experiment 2), https://zenodo.org/record/7752115 (experiment 3, AIRR setting) and https://zenodo.org/record/7727894 (experiment 3, transcriptomic setting).

Code availability

The source code for the experiments is openly available on GitHub at https://github.com/uio-bmi/causalairr.

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Acknowledgements

We acknowledge generous support by The Leona M. and Harry B. Helmsley Charitable Trust (grant no. 2019PG-T1D011 to V.G.), the UiO World-Leading Research Community (to V.G. and L.M.S.), the UiO:LifeScience Convergence Environment Immunolingo (to V.G. and G.K.S.), the UiO:LifeScience Convergence Environment RealArt (to G.K.S. and C.K.), EU Horizon 2020 iReceptorplus (grant no. 825821 to V.G. and L.M.S.), a Research Council of Norway FRIPRO project (grant no. 300740 to V.G.), a Norwegian Cancer Society Grant (215817 to V.G.), a Research Council of Norway IKTPLUSS project (grant no. 311341 to V.G. and G.K.S.) and Stiftelsen Kristian Gerhard Jebsen (K.G. Jebsen Coeliac Disease Research Centre, to L.M.S. and G.K.S.).

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Authors and Affiliations

  1. Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway

    Milena Pavlović, Ghadi S. Al Hajj, Chakravarthi Kanduri & Geir K. Sandve

  2. K.G. Jebsen Centre for Coeliac Disease Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway

    Milena Pavlović, Ludvig M. Sollid & Geir K. Sandve

  3. UiO:RealArt Convergence Environment, University of Oslo, Oslo, Norway

    Milena Pavlović, Chakravarthi Kanduri & Geir K. Sandve

  4. Department of Mathematics, University of Oslo, Oslo, Norway

    Johan Pensar

  5. Department of Pharmacy, University of Oslo, Oslo, Norway

    Mollie E. Wood

  6. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Mollie E. Wood

  7. Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway

    Ludvig M. Sollid & Victor Greiff

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Contributions

M.P., V.G. and G.K.S. conceived the study. M.P., G.S.A.H. and C.K. performed the experiments. J.P., M.E.W., L.M.S., C.K. and G.S.A.H. provided critical feedback. M.P., V.G. and G.K.S. drafted the manuscript. G.K.S. supervised the project. All authors read and approved the final manuscript and are personally accountable for its content.

Corresponding authors

Correspondence to Milena Pavlović or Geir K. Sandve.

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Competing interests

V.G. declares advisory board positions in aiNET GmbH, Enpicom BV, Absci, Omniscope and Diagonal Therapeutics. V.G. is a consultant for Adaptyv Biosystems, Specifica Inc., Roche/Genentech, immunai and LabGenius. The remaining authors declare no competing interests.

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Pavlović, M., Al Hajj, G.S., Kanduri, C. et al. Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nat Mach Intell 6, 15–24 (2024). https://doi.org/10.1038/s42256-023-00781-8

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