By Marie-France Sagot, Maria Emilia M.T. Walter

This booklet constitutes the refereed court cases of the second one Brazilian Symposium on Bioinformatics, BSB 2007, held in Angra dos Reis, Brazil, in August 2007; co-located with IWGD 2007, the overseas Workshop on Genomic Databases.

The thirteen revised complete papers and six revised prolonged abstracts have been rigorously reviewed and chosen from 60 submissions. The papers handle a large diversity of present themes in computationl biology and bioinformatics that includes unique examine in laptop 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 resources for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31,

Example text

The 24 G. A. Brizuela length of the motif (l) and the maximum number d of possible mutations in its occurrences. , pN } that correspond to the first character of each of the N occurrences that where found. Those N occurrences of the inserted (l, d)-motif. Each pi ∈ {1, 2, · · · , T − l + 1} ∪ {0}, with pi = 0 means that sequence Si contains no occurrences of the motif. Notice that this problem was already implicitly defined in [17]. Although the planted motif problem was not explicitly defined, the author introduced two variants of the motif finding problem and, one of them includes the motif finding problem as it is defined above.

That is, if the starting position for the occurrence predicted by the algorithms coincides with the actual position of that occurrence this error is zero. Otherwise, if the predicted position if shifted to the right then the error is positive, and when this predicted position is shifted to the left the error is negative. The same information is reported for the MbGA in rows four and five. PbGA and MbGA produce similar results; PbGA predicts one more position than MbGA does. However, MbGA obtains the best result in terms of the score.

The prior knowledge about a known structure of the data can be integrated into MOCLE by means of an additional objective function that takes external information into account. In this paper we investigated the information gain measure for this purpose [15,18]. The CR index, used in [7], would not be appropriate for our purpose. It considers negatively the subdivisions of clusters, while we want to find partitions that are refinement of the known partition. As in Demiriz et al. [6], we aim at generating partitions with clusters as pure as possible regarding the class distribution.

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