Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed

Cytokines are signaling molecules secreted and sensed by immune and other cell types, enabling dynamic inter-cellular communication. Although a vast amount of data on these interactions exists, this information is not compiled, integrated or easily searchable. Here we report immuneXpresso, a text mining engine that structures and standardizes knowledge of immune inter-cellular communication. We applied immuneXpresso to PubMed to identify relationships between 340 cell types and 140 cytokines across thousands of diseases. The method distinguishes between incoming and outgoing interactions, and includes the effect of the interaction and the cellular function involved. These factors are assigned a confidence score and linked to the disease. Leveraging the breadth of this network, we predict and experimentally verify previously unappreciated cell-cytokine interactions. We also build a global immune-centric view of diseases and use it to predict cytokine-disease associations. This standardized knowledgebase (www.immunexpresso.org) opens up new directions for interpretation of immune data and model-driven systems immunology.