  Background: Representing biological networks as graphs is a powerful approach
to reveal underlying patterns, signatures, and critical components from
high-throughput biomolecular data. However, graphs do not natively capture the
multi-way relationships present among genes and proteins in biological systems.
Hypergraphs are generalizations of graphs that naturally model multi-way
relationships and have shown promise in modeling systems such as protein
complexes and metabolic reactions. In this paper we seek to understand how
hypergraphs can more faithfully identify, and potentially predict, important
genes based on complex relationships inferred from genomic expression data
sets.
  Results: We compiled a novel data set of transcriptional host response to
pathogenic viral infections and formulated relationships between genes as a
hypergraph where hyperedges represent significantly perturbed genes, and
vertices represent individual biological samples with specific experimental
conditions. We find that hypergraph betweenness centrality is a superior method
for identification of genes important to viral response when compared with
graph centrality.
  Conclusions: Our results demonstrate the utility of using hypergraphs to
represent complex biological systems and highlight central important responses
in common to a variety of highly pathogenic viruses.
