By Sérgio Campos

This ebook constitutes the refereed court cases of the ninth Brazilian Symposium on Bioinformatics, BSB 2014, held in Belo Horizonte, Brazil, in October 2014. The 18 revised complete papers awarded have been conscientiously reviewed and chosen from 32 submissions. The papers conceal all facets of bioinformatics and computational biology.

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Extra resources for Advances in Bioinformatics and Computational Biology: 9th Brazilian Symposium on Bioinformatics, BSB 2014, Belo Horizonte, Brazil, October 28-30, 2014, Proceedings

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References 1. : Generic eukaryotic core promoter prediction using structural features of dna. Genome Research 18(2), 310–323 (2008) 2. : Prosom: core promoter prediction based on unsupervised clustering of dna physical profiles. Bioinformatics 24(13), i24–i31 (2008) 3. : Computational applications of dna structural scales. , Sensen, C. ) ISMB, pp. 35–42. AAAI (1998) 4. : Random forests. Machine Learning 45(1), 5–32 (2001) 5. : Support-vector networks. Machine Learning 20(3), 273–297 (1995) 6. : Ensemble approach combining multiple methods improves human transcription start site prediction.

A pattern-based nearest neighbor search approach for promoter prediction using dna structural profiles. Bioinf. 25(16), 2006–2012 (2009) 9. : A comparison study on feature selection of dna structural properties for promoter prediction. BMC Bioinformatics 13(1), 4 (2012) 10. : Core promoter t-blocks correlate with gene expression levels in c. elegans. Genome Research 21(5), 707–717 (2011) 11. : DNA structural properties in the classification of genomic transcription regulation elements. Bioinformatics and Biology Insights 6, 155–168 (2012) 12.

Unfortunately, too little is known about microbial interactions. In this sense, the metagenomic analysis conducts studies from of material extracted directly from the environment has been developed in order to extract S. ): BSB 2014, LNBI 8826, pp. 25–32, 2014. c Springer International Publishing Switzerland 2014 26 L. Corrêa et al. knowledge from this interaction. The metagenomic sequence process poses challenges that could be handled by the utilization of Machine Learning (ML) techniques. In fact, ML has been applied succesfully in several genomics problems.

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