Abstract

The analysis of ‘Big Data’ has great potential in drug discovery; however complications arise in integrating this data in a principled and coherent way. An important statistical tool to manage complexity is that of graph theory, which is as satisfying and attractive to the hard core data analyst as it is to the lay person. The statistician can marvel at the mathematics behind the theory while the lay-person can appreciate the highly visual and graphical information that shows links between objects and their relationships. In this paper we show how graphs and proteomic data are used to facilitate drug discovery.