Summary
One of the most important genomic tasks is the identification of promoters and splice-junction zone, which are essential on deciding whether there is a gene or not in a genome sequence. This problem could be seen as a classification problem, therefore the use of computational algorithms for both, pattern recognition and classification are a natural option to face it. In this chapter we develop a pattern classifier algorithm that works notably with bioinformatics databases. The associative memories model on which the classifier is based is the Alpha-Beta model. In order to achieve a good classification performance it was necessary to develop a new heteroassociative memories algorithm that let us recall the complete fundamental set. The heteroassociative memories property of recalling all the fundamental patterns is not so common; actually, no previous model of heteroassociative memory can guarantee this property. Thus, creating such a model is an important contribution. In addition, an heteroasociative Alpha-Beta multimemory is created, as a fundamental base for the proposed classifier.
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Godínez, I.R., López-Yáñez, I., Yáñez-Márquez, C. (2009). Classifying Patterns in Bioinformatics Databases by Using Alpha-Beta Associative Memories. In: Sidhu, A.S., Dillon, T.S. (eds) Biomedical Data and Applications. Studies in Computational Intelligence, vol 224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02193-0_8
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DOI: https://doi.org/10.1007/978-3-642-02193-0_8
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