The increasing amount and complexity of molecular biology data evokes a need to focus on automated methods for mining this data. In addition, molecular biology data is frequently structural in nature, or is composed of parts and relations between the parts. Hence, there exists a need to develop tools to analyze and discover concepts in structural databases. The goal of this research is to provide a system that mines databases represented as graphs. We demonstrate how the Subdue system can be used to perform three key techniques useful for mining molecular biology data: unsupervised pattern discovery, supervised concept learning from examples, and conceptual clustering. Applications of the Subdue system to protein databases demonstrate the eectiveness of the graph-based system to discover patterns in this data. 1 Introduction In recent years, there has been an explosive amount of molecular biology information obtained and deposited in various databases. Identifying and inter..
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