School of Computing Research Colloquia

Discovering disease mechanisms by clustering a bipartite graph of diseases and pathways

Christelle Gendrin, Daresbury Laboratory, Science and Technology Facilities Council

Abstract: The body of biomedical research published in PubMed contains a lot of information about relationships between diseases and genes. Those relationships can be summarized by the help of network. In this study we used a disease-pathway network constructed on PubMed MeSH term and a data-driven approach based on graph theory (trees and clustering) to infer new biological hypothesis. The trees summarization of the networks revealed large structure of the network which is either due to biological relationship but also coverage bias in the literature. Overall, the clusters revealed diseases with common mechanisms. These clusters imply some novel hypotheses about human disease biology.