Download Advances in Bioinformatics and Computational Biology: Second by Marie-France Sagot, Maria Emilia M.T. Walter PDF

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

This e-book constitutes the refereed complaints 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 foreign 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 deal with a extensive variety of present themes in computationl biology and bioinformatics that includes unique examine in computing device technological know-how, arithmetic and statistics in addition to in molecular biology, biochemistry, genetics, drugs, microbiology and different existence sciences.

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Extra info for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31,

Example text

Some others [2], [3], [19], use enumeration strategies. In [17] two algorithms based on the construction of suffix trees are proposed. Both time and space complexity bounds are improved in this paper comparing to Sagot et al. [18] and to the ones produced by Waterman et al. [22]. In [4] an algorithm based on random projections of the motif is proposed. Constructed under the planted motif model, this algorithm obtains a good performance compared with the commonly used methods like Gibbs [11] and MEME [1].

From the partitions created with these parameters values, we chose only those partitions having k in the interval of interest to be used in the initial population. In the experiments, we compare two versions of MOCLE: one unsupervised (MUH) and one that take prior knowledge into account (MSH). The only difference between them is that MSH includes the third objective function that depends on previous knowledge. The same initial configuration, including the initial population, was used for both versions.

We can see here that no significant improvement is achieved by the PbGA. In the same table results given by the Gibbs sampler [11] are also shown. We can see that the MbGA presents competitive results, outperforming the Gibbs sampler in five out of twelve instances. These results showing that MbGA is better than PbGA support the encoding selection made by other authors [10]. We also deal with a real problem consisting of 18 sequences of 104 base pairs each. There are repeats of the motif in 5 of the 18 sequences.

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