ImmuNet


NAR Molecular Biology Database Collection entry number 1913
Dmitriy Gorenshteyn, Elena Zaslavsky, Miguel Fribourg, Christopher Y. Park, Aaron K. Wong, Alicja Tadych, Boris M. Hartmann, Randy A. Albrecht, Adolfo GarcĂ­a-Sastre, Steven H. Kleinstein, Olga G. Troyanskaya, Stuart C. Sealfon

Database Description

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. 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.


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