Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases

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
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="" style="color: black;">Immunity</a> </i> &nbsp;
Graphical Abstract Highlights d Interactive web-accessible immunology resource leverages 38,088 experiments d Powerful computational methods generate big-data-driven hypotheses for immunology d Predicts new immune pathway interactions, mechanisms, and disease-associated genes d Flexible, user-friendly platform addresses diverse immunological research questions SUMMARY Many functionally important interactions between genes and proteins involved in immunological diseases and processes are
more &raquo; ... 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:// 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1016/j.immuni.2015.08.014</a> <a target="_blank" rel="external noopener" href="">pmid:26362267</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC4753773</a> <a target="_blank" rel="external noopener" href="">fatcat:adwa3kb5pnbw3mw65gppschhly</a> </span>
<a target="_blank" rel="noopener" href="" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href=""> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> </button> </a> <a target="_blank" rel="external noopener" href="" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> </button> </a>