Bio-medical Ontologies Maintenance and Change Management

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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. springerlink.com c○ Springer-Verlag Berlin Heidelberg 2009
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Fig. 3. Class Hierarchy of Protein
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Completing the Total Wellbeing Puzz
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Author Index Apiletti, Daniele 169
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