A multi-omics systems vaccinology resource to develop and test computational models of immunity.

Autor:	Shinde P; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Soldevila F; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Reyna J; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, San Diego, CA, USA., Aoki M; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Rasmussen M; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark., Willemsen L; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Kojima M; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Ha B; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Greenbaum JA; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Overton JA; Knocean Inc., 107 Quebec Avenue, Toronto, Ontario M6P 2T3, Canada., Guzman-Orozco H; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Nili S; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Orfield S; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Gygi JP; Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA., da Silva Antunes R; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA., Sette A; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA., Grant B; Department of Molecular Biology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, USA., Olsen LR; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark., Konstorum A; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA., Guan L; Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA., Ay F; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA., Kleinstein SH; Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA., Peters B; Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA. Electronic address: bpeters@lji.org.
Jazyk:	angličtina
Zdroj:	Cell reports methods [Cell Rep Methods] 2024 Mar 25; Vol. 4 (3), pp. 100731. Date of Electronic Publication: 2024 Mar 14.
DOI:	10.1016/j.crmeth.2024.100731
Abstrakt:	Systems vaccinology studies have identified factors affecting individual vaccine responses, but comparing these findings is challenging due to varying study designs. To address this lack of reproducibility, we established a community resource for comparing Bordetella pertussis booster responses and to host annual contests for predicting patients' vaccination outcomes. We report here on our experiences with the "dry-run" prediction contest. We found that, among 20+ models adopted from the literature, the most successful model predicting vaccination outcome was based on age alone. This confirms our concerns about the reproducibility of conclusions between different vaccinology studies. Further, we found that, for newly trained models, handling of baseline information on the target variables was crucial. Overall, multiple co-inertia analysis gave the best results of the tested modeling approaches. Our goal is to engage community in these prediction challenges by making data and models available and opening a public contest in August 2024. Competing Interests: Declaration of interests The authors declare no competing interests. (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu