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By Joao Carlos Setubal, Sergio Verjovski-Almeida

This booklet constitutes the refereed lawsuits of the Brazilian Symposium on Bioinformatics, BSB 2005, held in Sao Leopoldo, Brazil in July 2005.

The 15 revised complete papers and 10 revised prolonged abstracts awarded including three invited papers have been conscientiously reviewed and chosen from fifty five submissions. The papers handle a extensive diversity of present subject matters in computational biology and bioinformatics.

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Extra resources for Advances in Bioinformatics and Computational Biology: Brazilian Symposium on Bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings

Example text

In [6] and [11], SVMs were successfully employed in the localization of prokaryotic and eukaryotic proteins. SVMs were also applied recently in the localization of human proteins [9]. In [14], a NB classifier was used in the localization of proteins of five distinct organisms, and the generated predictors are part of a protein analysis web-service. C. Setubal and S. ): BSB 2005, LNBI 3594, pp. 42–53, 2005. c Springer-Verlag Berlin Heidelberg 2005 Protein Cellular Localization with Multiclass Support Vector Machines 43 chines (SVMs) [5].

2003). ods_key=cise051203 2. : Cybernetics: or Control and Communication in the Animal and the Machine. MIT Press (1948) 212 Cyberinfrastructure for PathoSystems Biology 27 3. Studholme, D. , Downie, J. , Preston, G. : Protein Domains and Architectural Innovation in Plant-Associated Proteobacteria. BMC Genomics. 6 (2005) 17 4. , Kumar, V. , Marathe, M. : Modelling Disease Outbreaks in Realistic Urban Social Networks. Nature. 429 (2004) 180-184. cmd=Retrieve&db=PubMed&dopt=Citatio n&list_uids=15141212 5.

In an alternative strategy, Dietterich and Bariki [7] proposed the use of a distributed output code to represent the k classes associated with a multiclass problem. For each class, a codeword of length l is assigned. Frequently, the size of the codewords has more bits than needed in order to represent each class uniquely. The additional bits can be used to correct eventual classification errors. For this reason, this method is named error-correcting output coding (ECOC). A new pattern x can be classified by evaluating the predictions of the l classifiers, which generate a string s of length l.

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