Bio-medical Ontologies Maintenance and Change Management

Classifying Patterns in Bioinformatics
Databases by Using Alpha-Beta Associative
Memories
Israel Román Godínez, Itzamá López-Yáñez, and Cornelio Yáñez-Márquez
Center for Computer Research, National Polytechnic Institute
iromanb05@sagitario.cic.ipn.mx
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.
1 Introduction
In the later decades, very important scientific advances in the field of molecular
biology have been achieved. Thanks to the enormous amounts of information
derived from these advances, there has arisen a need to process such
information in a faster way and just as effectively, or more, than by an expert.
This gives birth to a new branch of science, known as Bioinformatics:
a multidisciplinary field which combines, among others, two important fields
of science, molecular biology and computer sciences [1].
Among the first and foremost problems boarded by Bioinformatics are:
the development of databases, protein sequence alignment, DNA string
sequencing, protein structure prediction, protein structure classification, promoter
identification, splice-junction zone localization, and phylogenetic relationships
determining [2], [3].
The solutions to some of these problems are based on the search and
localization of patterns in certain sequences, in order to classify them. As
an example, we can mention promoter identification and splice-junction zone
A.S. Sidhu et al. (Eds.): Biomedical Data and Applications, SCI 224, pp. 187–210.
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