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ICDM
2006
IEEE

Application of Graph-based Data Mining to Metabolic Pathways

9 years 6 months ago
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...
Chang Hun You, Lawrence B. Holder, Diane J. Cook
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICDM
Authors Chang Hun You, Lawrence B. Holder, Diane J. Cook
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