Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases.

TitleInteractive Big Data Resource to Elucidate Human Immune Pathways and Diseases.
Publication TypeJournal Article
Year of Publication2015
AuthorsGorenshteyn, 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 Published2015 Sep 15
KeywordsAlgorithms, 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

<p>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 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. </p>

Alternate JournalImmunity
PubMed ID26362267
PubMed Central IDPMC4753773
Grant ListHHSN272201000054C / AI / NIAID NIH HHS / United States
1U19AI117873 / AI / NIAID NIH HHS / United States
R01 GM071966 / GM / NIGMS NIH HHS / United States
U19 AI117873 / AI / NIAID NIH HHS / United States
T32 MH096678 / MH / NIMH NIH HHS / United States