New PDF release: Advances in Bioinformatics and Computational Biology: 6th

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

ISBN-10: 3642228259

ISBN-13: 9783642228254

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

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

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The only difference among the nine datasets used in our experiments is the number of attributes. The first three datasets were obtained from data available on the Kaggle website3 and using the Stanford Sierra web service. These datasets, named D1 , D2 and D3 , are composed by the following predictive attributes: 1) D1 : HIV mutations, viral load and CD4+ cell counts; 2) HIV mutations, viral load, CD4+ cell counts and antiretroviral drug resistance levels; 3) HIV mutations, viral load, CD4+ cell counts, resistance levels to antiretroviral drugs, length of RT and PR sequence, similarity of RT and PR considering their reference sequences, HIV subtype and epitope.

Our benchmark includes all the annotated protein-coding genes with typical structure and no alternative splicing. To each sequence obtained, we left 1000bp of intergenic region at their 5 and 3 ends. The target sequences were taken from the HomoloGene Database [14] and correspond to sequences of cDNAs evolutionary related to the target gene. Finally, the set of blocks for each instance was constructed by means of a HMM-based algorithm that finds the potential exons of a given DNA sequence. This algorithm is part of a gene prediction tool called Genscan [1].

L. C. M. 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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Advances in Bioinformatics and Computational Biology: 6th Brazilian Symposium on Bioinformatics, BSB 2011, Brasilia, Brazil, August 10-12, 2011. Proceedings by David Langenberger, Sebastian Bartschat, Jana Hertel, Steve Hoffmann, Hakim Tafer (auth.), Osmar Norberto de Souza, Guilherme P. Telles, Mathew Palakal (eds.)


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