Temporal and structural analysis of biological networks in combination with microarray data

We introduce a graph-based relational learning approach using graph-rewriting rules for temporal and structural analysis of biological networks changing over time. The analysis of dynamic biological networks is necessary to understand life at the system-level, because biological networks continuously change their structures and properties, while an organism performs various biological activities. A dynamic graph represents dynamic properties as well as structural properties of biological networks. Microarray data can reflect dynamic properties of biological processes. Biological networks, which contain various molecules and relationships between molecules, show structural properties representing various relationships between entities. Most current graph-based data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. Most approaches for analysis of microarray data disregard structural properties on biological systems. But our dynamic graph-based relational learning approach describes how the graphs temporally and structurally change over time in the dynamic graph representing biological networks in combination with microarray data.