By Roberto T. Alves, Myriam R. Delgado, Alex A. Freitas (auth.), Ana L. C. Bazzan, Mark Craven, Natália F. Martins (eds.)
This publication constitutes the refereed court cases of the 3rd Brazilian Symposium on Bioinformatics, BSB 2008, held in Sao Paulo, Brazil, in August 2008 - co-located with IWGD 2008, the foreign Workshop on Genomic Databases.
The 14 revised complete papers and five prolonged abstracts have been conscientiously reviewed and chosen from forty-one submissions. The papers tackle a large diversity of present issues in computational biology and bioinformatics that includes unique examine in desktop technological know-how, arithmetic and data in addition to in molecular biology, biochemistry, genetics, drugs, microbiology and different existence sciences.
Read Online or Download Advances in Bioinformatics and Computational Biology: Third Brazilian Symposium on Bioinformatics, BSB 2008, Santo André, Brazil, August 28-30, 2008. Proceedings PDF
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Additional info for Advances in Bioinformatics and Computational Biology: Third Brazilian Symposium on Bioinformatics, BSB 2008, Santo André, Brazil, August 28-30, 2008. Proceedings
Machine Learning 1(1), 81–106 (1986) 18. 5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993) 19. : Fast eﬀective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123 (1995) 20. : An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000) 21. : Nearest neighbor pattern classiﬁcation, Information Theory. IEEE Transactions 13(1), 21–27 (1967) 22. : Bayesian Network Classiﬁers.
Conclusions In this paper, we presented a comparative study of six hierarchical classiﬁcation algorithms for diﬀerent kinds of protein signatures - Prosite, Pfam and Prints. 5, RIPPER, SVMs, KNN and BayesNet. The results from these algorithms were compared with the results of an algorithm based on a variation of the Top-Down approach named Top-Down Ensembles approach, which combines results from classiﬁers induced by the ﬁve ML techniques previously mentioned. In order to evaluate the performance of these algorithms, experiments were performed using three bioinformatics datasets, which are related with G-ProteinCoupled Receptors (GPCRs).
Discrete models are particulary ﬁt to perform high level structure search, since the search space is greatly reduced and very similar structures can be more easily avoided. The applicability of the discrete models relies on the solution of three diﬃculties, since discrete models: – Require a scoring function that can ignore the atomic details giving high scores to physically inexact, near native structures. – Need a search technique that can eﬃciently search the structure space without enumerating all the structures, since the space size is still exponential on the size of the protein.
Advances in Bioinformatics and Computational Biology: Third Brazilian Symposium on Bioinformatics, BSB 2008, Santo André, Brazil, August 28-30, 2008. Proceedings by Roberto T. Alves, Myriam R. Delgado, Alex A. Freitas (auth.), Ana L. C. Bazzan, Mark Craven, Natália F. Martins (eds.)