Pineda Sanjuan, SilviaBunis, Daniel G.Kosti, IditSirota, MarinaAltman, Russ B.2024-01-292024-01-292020Pineda, Silvia and Bunis, Daniel and Kosti, Idit and Sirota, Marina, Data Integration for Immunology (July 2020). Annual Review of Biomedical Data Science, Vol. 3, pp. 113-136.2574-341410.1146/annurev-biodatasci-012420-122454https://hdl.handle.net/20.500.14352/95941Over the last several years, next-generation sequencing and its recent push toward single-cell resolution have transformed the landscape of immunology research by revealing novel complexities about all components of the immune system. With the vast amounts of diverse data currently being generated, and with the methods of analyzing and combining diverse data improving as well, integrative systems approaches are becoming more powerful. Previous integrative approaches have combined multiple data types and revealed ways that the immune system, both as a whole and as individual parts, is affected by genetics, the microbiome, and other factors. In this review, we explore the data types that are available for studying immunology with an integrative systems approach, as well as the current strategies and challenges for conducting such analyses.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal articlehttps://doi.org/10.1146/annurev-biodatasci-012420-122454https://www.annualreviews.org/doi/10.1146/annurev-biodatasci-012420-122454restricted access612.017004.85ImmunologySystems immunologyData integrationMachine learningInmunologíaInteligencia artificial (Informática)2412 Inmunología1203.04 Inteligencia Artificial