Differential Association Rule Mining for the Study of Protein-Protein Interaction Networks

Protein-protein interactions are of great interest to biologists. A variety of high-throughput techniques have been devised, each of which leads to a separate definition of an interaction network. The concept of differential association rule mining is introduced to study the annotations of proteins in the context of one or more interaction networks. Differences among items across edges of a network are explicitly targeted. As a second step we identify differences between networks that are separately defined on the same set of nodes. The technique of differential association rule mining is applied to the comparison of protein annotations within an interaction network and between different interaction networks. In both cases we were able to find rules that explain known properties of protein interaction networks as well as rules that show promise for advanced study. General Terms association rule mining, protein interactions, relational data mining, graph-based data mining

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Christopher Besemann, Anne Denton, Ajay Yekkirala, Ron Hutchison, Marc Anderson
• Keywords: Association Rule Mining, Data Mining, Graph-based Data Mining, KDD 2004, Protein Interaction Networks