TY - CHAP
T1 - Classifying patterns in bioinformatics databases by using alpha-beta associative memories
AU - Godínez, Israel Román
AU - López-Yáñez, Itzamá
AU - Yáñez-Márquez, Cornelio
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=67650689328&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02193-0_8
DO - 10.1007/978-3-642-02193-0_8
M3 - Capítulo
AN - SCOPUS:67650689328
SN - 9783642021923
T3 - Studies in Computational Intelligence
SP - 187
EP - 210
BT - Biomedical Data and Applications
A2 - Sidhu, Amandeep
A2 - Dilliom, Tharam
ER -