An association rule based model for information extraction from protein sequence data
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Becerra, David
Cantor Monroy, Giovanni Antonio
Niño, Luis Fernando
Gómez Perdomo, Jonatan
Bobadilla, Leonardo
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EspañolFecha de publicación
2008
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In this paper, a data mining technique for protein sequence pattern extraction is developed. Specifically, the aim is to explore the use of association rules as a basis to build successful secondary structure predictors, in a sequencestructure layer. No heuristic or biological infor mation is taken into account in the present study and only the information given by the association rules is used as a basis for building a secondary structure predictor. This work gives some insights about secondary structure prediction features to be used in learning algorithms; this is expected to be useful to achieve substantial improvements of accuracy in protein secondary structure prediction.