Advances in Bioinformatics and Computational Biology: Third by Roberto T. Alves, Myriam R. Delgado, Alex A. Freitas

By Roberto T. Alves, Myriam R. Delgado, Alex A. Freitas (auth.), Ana L. C. Bazzan, Mark Craven, Natália F. Martins (eds.)

This booklet constitutes the refereed complaints of the 3rd Brazilian Symposium on Bioinformatics, BSB 2008, held in Sao Paulo, Brazil, in August 2008 - co-located with IWGD 2008, the overseas 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 huge diversity of present issues in computational biology and bioinformatics that includes unique learn in machine technological know-how, arithmetic and information in addition to in molecular biology, biochemistry, genetics, medication, microbiology and different lifestyles sciences.

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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

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Pdf 24. P. Costa et al. 25. org 26. : Hierarchical multi-classification. In: Proceedings of the ACM SIGKDD 2002 Workshop on Multi-Relational Data Mining (MRDM 2002), pp. 21–35 (2002) 27. : Inference for the Generalization Error. Machine Learning 52(3), 239–281 (2003) 28. : On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach.

It occurs because of the presence of several leaf nodes in the third level of these hierarchies. Some leaf-nodes are also present in the first and second levels. All datasets were divided according to the 5-fold cross-validation methodology. Accordingly, each dataset is divided into five parts of approximately equal size. At each round, one fold is left for test and the remaining folds are used in the classifiers training. This makes a total of five train and test sets. The final accuracy rate of a classification model is given by the mean of the predictive accuracies on the test sets from cross-validation.

A sophisticated approach consists of inducing a classification model for this prediction. This paper applies five hierarchical classification methods based on the standard Top-Down approach and one hierarchical classification method based on a new approach named Top-Down Ensembles - based on the hierarchical combination of classifiers - to three different protein functional classification datasets that employ protein signatures. The algorithm based on the Top-Down Ensembles approach presented slightly better results than the other algorithms, indicating that combinations of classifiers can improve the performance of hierarchical classification models.

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