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Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases

Author(s): Gorenshteyn, D; Zaslavsky, E; Fribourg, M; Park, CY; Wong, Aaron K.; et al

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dc.contributor.authorGorenshteyn, D-
dc.contributor.authorZaslavsky, E-
dc.contributor.authorFribourg, M-
dc.contributor.authorPark, CY-
dc.contributor.authorWong, Aaron K.-
dc.contributor.authorTadych, A-
dc.contributor.authorHartmann, BM-
dc.contributor.authorAlbrecht, RA-
dc.contributor.authorGarcía-Sastre, A-
dc.contributor.authorKleinstein, SH-
dc.contributor.authorTroyanskaya, Olga G.-
dc.contributor.authorSealfon, SC-
dc.date.accessioned2018-07-20T15:08:15Z-
dc.date.available2018-07-20T15:08:15Z-
dc.date.issued2015-09-15en_US
dc.identifier.citationGorenshteyn, D, Zaslavsky, E, Fribourg, M, Park, CY, Wong, AK, Tadych, A, Hartmann, BM, Albrecht, RA, García-Sastre, A, Kleinstein, SH, Troyanskaya, OG, Sealfon, SC. (2015). Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases. Immunity, 43 (605 - 614. doi:10.1016/j.immuni.2015.08.014en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1q088-
dc.description.abstractMany functionally important interactions between genes and proteins involved in immunological diseases and processes are unknown. The exponential growth in public high-throughput data offers an opportunity to expand this knowledge. To unlock human-immunology-relevant insight contained in the global biomedical research effort, including all public high-throughput datasets, we performed immunological-pathway-focused Bayesian integration of a comprehensive, heterogeneous compendium comprising 38,088 genome-scale experiments. The distillation of this knowledge into immunological networks of functional relationships between molecular entities (ImmuNet), and tools to mine this resource, are accessible to the public at http://immunet.princeton.edu. The predictive capacity of ImmuNet, established by rigorous statistical validation, is easily accessed by experimentalists to generate data-driven hypotheses. We demonstrate the power of this approach through the identification of unique host-virus interaction responses, and we show how ImmuNet complements genetic studies by predicting disease-associated genes. ImmuNet should be widely beneficial for investigating the mechanisms of the human immune system and immunological diseases. The large amount of publically available high-throughput data contains, in aggregate, a vast amount of immunologically relevant insight. Sealfon and colleagues report ImmuNet, a web-accessible public resource based on 38,088 experiments that allows researchers to predict gene-gene relationships relevant to the human immune system and immunological diseases.en_US
dc.format.extent605 - 614en_US
dc.language.isoen_USen_US
dc.relation.ispartofImmunityen_US
dc.rightsAuthor's manuscripten_US
dc.titleInteractive Big Data Resource to Elucidate Human Immune Pathways and Diseasesen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1016/j.immuni.2015.08.014-
dc.date.eissued2015-09-15en_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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