By David Langenberger, Sebastian Bartschat, Jana Hertel, Steve Hoffmann, Hakim Tafer (auth.), Osmar Norberto de Souza, Guilherme P. Telles, Mathew Palakal (eds.)

This ebook constitutes the lawsuits of the sixth Brazilian Symposium on Bioinformatics, BSB 2011, held in Brasília, Brazil, in August 2011.
The eight complete papers and four prolonged abstracts awarded have been conscientiously peer-reviewed and chosen for inclusion during this e-book. The BSB subject matters of curiosity hide many parts of bioinformatics that variety from theoretical features of difficulties in bioinformatics to functions in molecular biology, biochemistry, genetics, and linked subjects.

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Additional info for Advances in Bioinformatics and Computational Biology: 6th Brazilian Symposium on Bioinformatics, BSB 2011, Brasilia, Brazil, August 10-12, 2011. Proceedings

Example text

Finally, based on Kurtz et al. [2], for the same implementations conditions, our algorithm is expected to perform better than Inenaga’s algorithm [5]. References 1. : Suffix trees on words. Algorithmica 23, 102–115 (1999) 2. : From Ukkonen to McCreight and Weiner: A unifying view of linear-time suffix tree construction. Algorithmica 19(3), 331–353 (1997) 3. : Algorithms on strings, trees, and sequences. Cambridge University Press, Cambridge (1997), computer science and computational biology 4. : Finding missing patterns.

Tk } of target sequences, a set B = {B1 , . . , Bb } of blocks (substrings) of S and a scoring function w, find a chain Γ of strings from B such that ki=1 scorew (Γ ∗ , Ti ) is maximum among all chains of blocks from B. Observe that, differently from the SAP, a solution to the MSAP will correspond to a chain of exons that best fit a set of transcript sequences (and not just one transcript sequence). Given this multiple sources of evidence, it is reasonable to assume that the probability of a solution to the MSAP be related with the searched gene is higher when compared with a solution to the SAP.

Bouillet trained with information about mutations in the viral genome accurately predict the patients’ response to therapies? Does the addition of resistance levels to antiretroviral drugs, the length of RT and PR sequences, similarity of the RT and PR to their reference sequences, HIV subtype, and epitope information significantly enhance the accuracy of the classifiers? To answer these questions the following methodology was adopted. From the original dataset we generated three different datasets varying the number of attributes.

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