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Curr Opin Biotechnol. 2018 Aug;52:109-115. doi: 10.1016/j.copbio.2018.03.009. Epub 2018 Apr 12.

Advancing systems immunology through data-driven statistical analysis.

Author information

1
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
2
Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA. Electronic address: kathryn.miller-jensen@yale.edu.

Abstract

Systems biology provides an effective approach to decipher, predict, and ultimately manipulate the complex and inter-connected networks that regulate the immune system. Advances in high-throughput, multiplexed experimental techniques have increased the availability of proteomic and transcriptomic immunological datasets, and as a result, have also accelerated the development of new data-driven computational algorithms to extract biological insight from these data. This review highlights how data-driven statistical models have been used to characterize immune cell subsets and their functions, to map the signaling and intercellular networks that regulate immune responses, and to connect immune cell states to disease outcomes to generate hypotheses for novel therapeutic strategies. We focus on recent advances in evaluating immune cell responses following viral infection and in the tumor microenvironment, which hold promise for improving vaccines, antiviral and cancer immunotherapy.

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