Learning patterns in the dynamics of biological networks

Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic networks is important not only to understand life at the system-level, but also to discover novel patterns in other structural data. Most current graph-based data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. Our approach analyzes a sequence of graphs and discovers rules that capture the changes that occur between pairs of graphs in the sequence. These rules represent the graph rewrite rules that the first graph must go through to be isomorphic to the second graph. Then, our approach feeds the graph rewrite rules into a machine learning system that learns general transformation rules describing the types of changes that occur for a class of dynamic biological networks. The discovered graph-rewriting rules show how biological networks change over t...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Chang Hun You, Lawrence B. Holder, Diane J. Cook
• Keywords: Data Mining, Dynamic Biological Networks, Graph Rewrite Rules, Graph Rewriting Rule, KDD 2009