|Title||Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||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|
|Date Published||2015 Sep 15|
|Keywords||Algorithms, Bayes Theorem, Computational Biology, Gene Regulatory Networks, Host-Pathogen Interactions, Humans, Immune System, Immune System Diseases, Internet, Protein Interaction Mapping, Protein Interaction Maps, Reproducibility of Results, Signal Transduction, Support Vector Machine, Transcriptome, Virus Diseases|
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.