Bio-medical Ontologies Maintenance and Change Management

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208 I.R. Godínez et al. whole fundamental set. This is then used to build the alpha-beta heteroassociative multimemories, foundation of the new pattern classifier, the alphabeta multimemories classifier. This new classifier is shown, through an experimental analysis, to exhibit a competitive classification performance in the tasks of promoter identification and splice-junction zone localization, even surpassing most of the best classifiers in these tasks, currently present in scientific literature. This competitive performance presents the opportunity to apply the AB- MMC algorithm to other tasks in Bioinformatics, with foreseeable competitive results. On the other hand, since not all the comparisons were favorable, it remains as future work to improve the classifier and better tune it to the tasks and databases on which it was tested here. Acknowledgements. The authors would like to thank the Instituto Politécnico Nacional (Secretaría Académica, COFAA, SIP, and CIC), the CONACyT, and SNI for their economical support to develop this work. References 1. Baldi, P., Brunak, S.: Bioinformatics: the machine learning approach. MIT Press, Cambridge (1998) 2. Doolittle, R.F.: Of URFs and ORFs: A primer on how to analyze derived amino acid sequences. University Science Books, Mill Valley (1987) 3. Wolfsberg, T.G., Wetterstrand, K.A., Guyer, M.S., Collins, F.S., Baxevanis, A.D.: A user’s guide to the human genome. Nature Genetics 32 (2002) 4. Hassoun, M.H.: Associative Neural Memories. Oxford University Press, New York (1993) 5. Kohonen, T.: Self-Organization and Associative Memory. Springer, Berlin (1989) 6. Ritter, G.X., Sussner, P., Diaz-de-Leon, J.L.: Morphological associative memories. IEEE Transactions on Neural Networks 9, 281–293 (1998) 7. Lesk, A.M.: Introduction to Bioinformatics, 2nd edn. Oxford University Press, Oxford (2005) 8. Altman, R.B., Valencia, A., Miyano, S., Ranganathan, S.: Challenges for intelligent system in biology. IEEE Intell. Syst. 16(6), 14–20 (2001) 9. Setubal, J., Meidanis, J.: Introduction to computational molecular biology. International Thomson Publishing, Boston (1999) 10. Ray, S.S., Bandyopadhyay, S., Mitra, P., Pal, S.K.: Bioinformatics in neurocomputing framework. In: Proceedings. IEEE, Los Alamitos (2005) 11. Mitra, S., Hayashi, Y.: Bioinformatics with soft computing. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 616–635 (2006) 12. Salzberg, S.L., Searls, D.B., Kasif, S.: Computational methods in molecular biology. Elsevier Science, Amsterdam (1998) 13. Cohen, J.: Bioinformatics—An Introduction for Computer Scientists. ACM Computing Surveys 36(2), 122–158 (2004)
Classifying Patterns in Bioinformatics Databases 209 14. Fickett, J.W.: Finding genes by computer: the state of the art. Trends Genet. 12(8), 316–320 (1996) 15. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. In: Proceedings of the National Academy of Sciences, vol. 79, pp. 2554–2558 (1982) 16. Jones, D.T.: GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. J. Mol. Biol. 287, 797–815 (1999) 17. Lukashin, A.V., Anshelevich, V.V., Amirikyan, B.R., Gragerov, A.I., Frank- Kamenetskii, M.D.: Neural network models for promoter recognition. J. Biomol. Struct. Dyn. (6), 1123–1133 (1989) 18. Sun, J., Song, W.Y., Zhu, L.H., Chen, R.S.: Analysis of tRNA gene sequences by neural network. J. Comput. Biol. 2(3), 409–416 (1995) 19. Kalate, R.N., Tambe, S.S., Kulkarni, B.D.: Artificial neural networks for prediction of mycobacterial promoter sequences. Comput. Biol. Chem. 27(6), 555–564 (2003) 20. Qian, N., Sejnowski, T.J.: Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202(4), 865–884 (1988) 21. Kohonen, T.: Correlation matrix memories. IEEE Transactions on Computers, C-21 4, 353–359 (1972) 22. Yánez, C.: Memorias Asociativas basadas en Relaciones de Orden y Operadores Binarios (In Spanish) Ph.D. Thesis. IPN Centro de Investigación en Computación, México (2002) 23. Yáñez-Márquez, C., Díaz de León-Santiago, J.L.: Memorias Asociativas Basadas en Relaciones de Orden y Operaciones Binarias. Computación y Sistemas 6(4), 300–311 (2003) 24. Yáñez-Márquez, C., Díaz de León-Santiago, J.L.: Memorias Asociativas con Respuesta Perfecta y Capacidad Infinita. In: Albornoz, A., Alvarado, M. (eds.) Taller de Inteligencia Artificial TAINA 1999, México, pp. 245–257 (1999) 25. Yáñez-Márquez, C., de León-Santiago, D.J.L., Salgado, J.C.: New V-Alpha- Beta Associative Memories able to Learn and Recall Patterns with Integer Components. Technical Report 210, Blue Series. National Polytechnic Institute. Center for Computing Research, Mé xico (2005) 26. Sossa, H., Barrón, R., Cuevas, F., Aguilar, C.: Associative Gray Level Pattern Processing Using Binary Decomposition and αβ Memories. Neural Processing Letters 22(1) (2005) 27. Yáñez-Márquez, C., de Leó n-Santiago, D.J.L., Salgado, J.C.: Applying the New V-Alpha-Beta Associative Memories to Gray Level Images. Technical Report 209, Blue Series. National Polytechnic Institute. Center for Computing Research, México (2005) 28. Yáñez-Márquez, C., Cruz-Meza, M.E., Sánchez-Garfias, F.A., López-Yáñez, I.: Using Alpha-Beta Associative Memories to Learn and Recall RGB Images. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 828–833. Springer, Heidelberg (2007) 29. Román Godínez, I.: Aplicación de los Modelos Asociativos Alfa-Beta a la Bioinformática (In Spanish) MsC Thesis. IPN Centro de Investigación en Computación, México (2007) 30. Román-Godínez, I., Yáñez-Márquez, C.: Complete Recall on Alpha-Beta Heteroassociative Memory. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS, vol. 4827, pp. 193–202. Springer, Heidelberg (2007)
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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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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2 A.S. Sidhu, M. Bellgard, and T.S.
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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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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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Bio-medical Ontologies Maintenance
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6 Conclusion Substructure Analysis
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Substructure Analysis of Metabolic
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Completing the Total Wellbeing Puzz
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296 M. Fernandez, M. Villasana, and
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Author Index Apiletti, Daniele 169
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