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.
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Authors | Dmitriy Gorenshteyn, Elena Zaslavsky, Miguel Fribourg, Christopher Y Park, Aaron K Wong, Alicja Tadych, Boris M Hartmann, Randy A Albrecht, Adolfo García-Sastre, Steven H Kleinstein, Olga G Troyanskaya, Stuart C Sealfon |
Journal | Immunity
(Immunity)
Vol. 43
Issue 3
Pg. 605-14
(Sep 15 2015)
ISSN: 1097-4180 [Electronic] United States |
PMID | 26362267
(Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)
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Copyright | Copyright © 2015 Elsevier Inc. All rights reserved. |
Topics |
- Algorithms
- Bayes Theorem
- Computational Biology
(methods)
- Gene Regulatory Networks
(genetics, immunology)
- Host-Pathogen Interactions
(immunology)
- Humans
- Immune System
(immunology, metabolism)
- Immune System Diseases
(genetics, immunology)
- Internet
- Protein Interaction Mapping
(methods)
- Protein Interaction Maps
(genetics, immunology)
- Reproducibility of Results
- Signal Transduction
(genetics, immunology)
- Support Vector Machine
- Transcriptome
(genetics, immunology)
- Virus Diseases
(genetics, immunology, virology)
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