Machine Learning Approaches to TCR Repertoire Analysis.

Journal: Frontiers in immunology
PMID: 35911778

Abstract

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Authors

  • Yotaro Katayama
    Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
  • Ryo Yokota
    National Research Institute of Police Science, Kashiwa, Chiba, Japan.
  • Taishin Akiyama
    Laboratory for Immune Homeostasis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
  • Tetsuya J Kobayashi
    Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan.

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