4.8 Article

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

期刊

NATURE MACHINE INTELLIGENCE
卷 3, 期 11, 页码 936-+

出版社

NATURE PORTFOLIO
DOI: 10.1038/s42256-021-00413-z

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资金

  1. Leona M. and Harry B. Helmsley Charitable Trust [2019PG-T1D011]
  2. UiO World-Leading Research Community
  3. UiO:LifeScience Convergence Environment Immunolingo
  4. EU Horizon 2020 iReceptorplus [825821]
  5. Research Council of Norway FRIPRO project [300740]
  6. Research Council of Norway IKTPLUSS project [311341]
  7. National Institutes of Health [P01 AI042288, HIRN UG3 DK122638]
  8. Stiftelsen Kristian Gerhard Jebsen (K.G. Jebsen Coeliac Disease Research Centre)

向作者/读者索取更多资源

immuneML is an AIRR-based machine learning tool that addresses reproducibility, transparency, and interoperability issues in the field of AIRR ML through an extensible, open-source software ecosystem. Users can utilize immuneML through a command-line tool or a Galaxy web interface, with extensive workflow documentation provided to facilitate widespread adoption.
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 (immuneml.uio.no) 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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