Knowledge Discovery in GENBANK

J. S. Aaronson, J. Haas, and G. C. Overton

We describe various methods designed to discover knowledge in the GenBank nucleic acid sequence database. Using a grammatical model of gene structure, we create a parse tree of a gene using features listed in the FEATURE table. The parse tree infers features that are not explicitly listed, but which follow from the listed features. This method discovers 30% more introns and 40% more exons when applied to a globin gene subset of GenBank. Parse tree construction also entails resolving ambiguity and inconsistency within a FEATURE table. We transform the parse tree into an augmented FEATURE table that represents inferred gene structure explicitly and unambiguously, thereby greatly improving the utility of the FEATURE table to researchers. We then describe various analogical reasoning techniques designed to exploit the homologous nature of genes. We build a classification hierarchy that reflects the evolutionary relationship between genes. Descriptive grammars of gene classes are then induced from the instance grammars of genes. Case based reasoning techniques use these abstract gene class descriptions to predict the presence and location of regulatory features not listed in the FEATURE table. A cross-validation test shows a success rate of 87% on a globin gene subset of GenBank.


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