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|Abstract:||Many 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.|
|Electronic Publication Date:||15-Sep-2015|
|Citation:||Gorenshteyn, 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.2015.08.014|
|Pages:||605 - 614|
|Type of Material:||Journal Article|
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