Tensor-based insights into systems immunity and infectious disease

Jackson L Chin; Liana C Chan; Michael R Yeaman; Aaron S Meyer

doi:10.1016/j.it.2023.03.003

Tensor-based insights into systems immunity and infectious disease

Trends Immunol. 2023 May;44(5):329-332. doi: 10.1016/j.it.2023.03.003. Epub 2023 Mar 29.

Authors

Jackson L Chin¹, Liana C Chan², Michael R Yeaman², Aaron S Meyer³

Affiliations

¹ Department of Bioengineering, University of California Los Angeles (UCLA), Los Angeles, CA 90024, USA.
² The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA 90502, USA; Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA; Division of Infectious Diseases, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, USA; Division of Molecular Medicine, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, USA.
³ Department of Bioengineering, University of California Los Angeles (UCLA), Los Angeles, CA 90024, USA; Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA 90024, USA; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA 90024, USA. Electronic address: ameyer@asmlab.org.

Abstract

Profiling immune responses across several dimensions, including time, patients, molecular features, and tissue sites, can deepen our understanding of immunity as an integrated system. These studies require new analytical approaches to realize their full potential. We highlight recent applications of tensor methods and discuss several future opportunities.

Keywords: canonical polyadic decomposition; data integration; dimensionality reduction; systems immunology; tensor decomposition.

Tensor-based insights into systems immunity and infectious disease

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