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<p>Given a subset P of a database, the problem of finding the query phi in a given database attribute having the closest extension to P is addressed. In the particular case that is outlined, P is the set of protein sequences in a protein sequence database matching a given protein sequence pattern, whereas phi is a query in the annotation of the database. Ideally, phi is the description of a biological function. If the extension of phi is very similar to P, an association between the pattern and the biological function described by the query may be inferred. An algorithm that efficiently searches the query space when negation is not considered is developed. Since the query language is a first-order language, the query space may be mapped into a set algebra in which a measure of stochastic dependence-an asymptotic approximation of the correlation coefficient-is used as a measure of set similarity. The algorithm uses the algebraic properties of such a measure to reduce the time required to search the query space. A prototype implementation of the algorithm has been tested in different collections of protein sequence patterns.</p>
correlation inference; annotation query; protein sequence pattern analysis; stochastic dependence measurement; set similarity measure; database queries; protein sequence database; query language; first-order language; query space; set algebra; asymptotic approximation; correlation coefficient; algebra; biology computing; database theory; proteins; query processing; set theory

T. Smith and R. Guigó, "Inferring Correlation Between Database Queries: Analysis of Protein Sequence Patterns," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 1030-1041, 1993.
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