Data integration for immunology
dc.contributor.author | Pineda Sanjuan, Silvia | |
dc.contributor.author | Bunis, Daniel G. | |
dc.contributor.author | Kosti, Idit | |
dc.contributor.author | Sirota, Marina | |
dc.contributor.editor | Altman, Russ B. | |
dc.date.accessioned | 2024-01-29T11:38:35Z | |
dc.date.available | 2024-01-29T11:38:35Z | |
dc.date.issued | 2020 | |
dc.description.abstract | Over 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. | en |
dc.description.department | Depto. de Estadística y Ciencia de los Datos | |
dc.description.faculty | Fac. de Estudios Estadísticos | |
dc.description.refereed | TRUE | |
dc.description.sponsorship | Burroughs Wellcome Fund | |
dc.description.status | pub | |
dc.identifier.citation | Pineda, 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. | |
dc.identifier.doi | 10.1146/annurev-biodatasci-012420-122454 | |
dc.identifier.issn | 2574-3414 | |
dc.identifier.officialurl | https://doi.org/10.1146/annurev-biodatasci-012420-122454 | |
dc.identifier.relatedurl | https://www.annualreviews.org/doi/10.1146/annurev-biodatasci-012420-122454 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/95941 | |
dc.issue.number | 1 | |
dc.journal.title | Annual review of biomedical data science | |
dc.language.iso | eng | |
dc.page.final | 136 | |
dc.page.initial | 113 | |
dc.publisher | Annual Reviews, Palo Alto, CA | |
dc.relation.projectID | K01LM012381 | |
dc.relation.projectID | P30 AR070155 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | en |
dc.rights.accessRights | restricted access | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject.cdu | 612.017 | |
dc.subject.cdu | 004.85 | |
dc.subject.keyword | Immunology | |
dc.subject.keyword | Systems immunology | |
dc.subject.keyword | Data integration | |
dc.subject.keyword | Machine learning | |
dc.subject.ucm | Inmunología | |
dc.subject.ucm | Inteligencia artificial (Informática) | |
dc.subject.unesco | 2412 Inmunología | |
dc.subject.unesco | 1203.04 Inteligencia Artificial | |
dc.title | Data integration for immunology | en |
dc.type | journal article | |
dc.type.hasVersion | VoR | |
dc.volume.number | 3 | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 9ff02bb9-3623-452e-ad72-8bb19687ec4e | |
relation.isAuthorOfPublication.latestForDiscovery | 9ff02bb9-3623-452e-ad72-8bb19687ec4e |
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