Mining adaptive immune receptor repertoires for biological and clinical information using machine learning

Victor Greiff, Gur Yaari, Lindsay G. Cowell

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

The adaptive immune system stores invaluable information about current and past immune responses and may serve as an ultrasensitive biosensor. Given the immune system's critical role in a wide variety of disease types, this has broad implications for biomedicine. Machine and deep learning is being leveraged to decipher how information is encoded in adaptive immune receptor repertoires to enable prediction from adaptive immune responses and fast-track vaccine, therapeutics, and diagnostics development. Recent advances include predicting the presence of immunity after vaccination or infection, predicting the presence of disease, and designing antibody-based therapeutics. Outstanding challenges encompass increasing our knowledge of the feature space structure that encodes relevant immune information, addressing the lack of ground truth–labeled data, and improving our handling of genetic and environmental confounding factors.

Original languageEnglish (US)
Pages (from-to)109-119
Number of pages11
JournalCurrent Opinion in Systems Biology
Volume24
DOIs
StatePublished - Dec 2020

Keywords

  • AIRR-seq
  • Adaptive immune receptor repertoires
  • Immunodiagnostics
  • Machine learning

ASJC Scopus subject areas

  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Drug Discovery
  • Computer Science Applications
  • Applied Mathematics

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