• DOI:10.1145/2649387.2660817
• Corpus ID: 30593231

Using 2-node hypergraph clustering coefficients to analyze disease-gene networks

@article{Gallagher2014Using2H,
  title={Using 2-node hypergraph clustering coefficients to analyze disease-gene networks},
  author={Suzanne Renick Gallagher and Micah Dombrower and Debra Goldberg},
  journal={Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics},
  year={2014}
}

• S. Gallagher, Micah Dombrower, D. Goldberg
• Published 20 September 2014
• Computer Science
• Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Disease-gene networks are bipartite networks that connect diseases to the genes with which they are associated. Due to the absence of tools to analyze bipartite networks, however, they are usually analyzed by "projecting" the bipartite network into a unipartite one by taking one side of the bipartition as the nodes and connecting two nodes if they share a common neighbor in the bipartite network. This projection, however, loses information, such as exactly which groups of diseases share a common gene. Here, we present an alternative method to analyze these networks by using hypergraph techniques. We review several metrics from previous work and demonstrate their ability to find related diseases in disease-gene network.