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The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires

A preprint version of the article is available at bioRxiv.


Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. So far, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency and interoperability. immuneML ( addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (1) reproducing a large-scale study on immune state prediction, (2) developing, integrating and applying a novel deep learning method for antigen specificity prediction and (3) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

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Fig. 1: Overview of immuneML.
Fig. 2: Use cases demonstrating ML model training, benchmarking and platform extension.

Data availability

All data for the analyses presented in the manuscript are openly available. The detailed result files for use cases 1–3 are available as zip files at (ref. 78; use case 1), (ref. 81; use case 2) and (ref. 82; use case 3). Input data for use case 1 was downloaded from

Code availability

The immuneML source code is openly available at Github ( under a free software license (AGPL-3.0). immuneML version 2.0.2 has been deposited on Zenodo with (ref. 75). The immuneML Python package can be downloaded from


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We acknowledge generous support by The Leona M. and Harry B. Helmsley Charitable Trust (grant number 2019PG-T1D011, to V.G. and T.M.B.), 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.), EU Horizon 2020 iReceptorplus (grant number 825821, to V.G. and L.M.S.), a Research Council of Norway FRIPRO project (grant number 300740, to V.G.), a Research Council of Norway IKTPLUSS project (grant number 311341, to V.G. and G.K.S.), the National Institutes of Health (grant numbers P01 AI042288 and HIRN UG3 DK122638 to T.M.B.) and Stiftelsen Kristian Gerhard Jebsen (K.G. Jebsen Coeliac Disease Research Centre, to L.M.S. and G.K.S.). We acknowledge support from ELIXIR Norway in recognizing immuneML as a national node service.

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Authors and Affiliations



M.P., V.G. and G.K.S. conceived the study. M.P. and G.K.S. designed the overall software architecture. M.P., L.S. and K.M. developed the main platform code. M.P. and L.S. performed all analyses. M.P., L.S., C.K., F.L.M.B., R.A., G.S.A.H., G.B., M.C., R.F., I.G., S.G., P.-H.H., K.R., E.R., P.A.R., A.S., D.T., C.R.W. and M.W. created software or documentation content. R.K., N.V., K.W., L.S., M.P., A.A.C. and B.C. designed and developed the Galaxy tools. C.K., R.A., T.M.B., M.C., S.C., L.G.C., I.H.H., E.H., G.K., M.L.K., C.L.-A., A.M., T.M., J.P., K.R., P.A.R., A.R., I.S., L.M.S. and G.Y. provided critical feedback. M.P., L.S., V.G. and G.K.S. drafted the manuscript. V.G. and G.K.S. supervised the project. All authors read and approved the final manuscript and are personally accountable for its content.

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Correspondence to Geir Kjetil Sandve.

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V.G. declares advisory board positions in aiNET GmbH and Enpicom B.V., and is a consultant for Roche/Genentech.

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Peer review information Nature Machine Intelligence thanks Pieter Meysman, Ryan Emerson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Pavlović, M., Scheffer, L., Motwani, K. et al. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat Mach Intell 3, 936–944 (2021).

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