Application of Graph-based Data Mining to Metabolic Pathways

We present a method for finding biologically meaningful patterns on metabolic pathways using the SUBDUE graph-based relational learning system. A huge amount of biological data that has been generated by long-term research encourages us to move our focus to a systems-level understanding of bio-systems. A biological network, containing various biomolecules and their relationships, is a fundamental way to describe bio-systems. Multi-relational data mining finds the relational patterns in both the entity attributes and relations in the data. A graph consisting of vertices and edges between these vertices is a natural data structure to represent biological networks. This paper presents a graph representation of metabolic pathways to contain all features, and describes the application of graph-based relational learning algorithms in both supervised and unsupervised scenarios. Supervised learning finds the unique substructures in a specific type of pathway, which help us understand bett...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Chang Hun You, Lawrence B. Holder, Diane J. Cook
• Keywords: Data Mining, Graph-based Relational Learning, ICDM 2006, Metabolic Pathways, Relational Learning System