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2013 IEEE 13th International Conference on Data Mining Workshops (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 169-173
Diane J. Cook , Washington State University
Chang Hun You , Washington State University
Lawrence B. Holder , Washington State University
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 better how pathways differ. Unsupervised learning shows hierarchical clusters that describe the common substructures in a specific type of pathway, which allow us to better understand the common features in pathways.
Diane J. Cook, Chang Hun You, Lawrence B. Holder, "Application of Graph-based Data Mining to Metabolic Pathways", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 169-173, 2006, doi:10.1109/ICDMW.2006.31
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