Advances in Bioinformatics and Computational Biology: Second by K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de

By K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de Souza (auth.), Marie-France Sagot, Maria Emilia M. T. Walter (eds.)

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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, 2007. Proceedings

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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].

E2 contains a refinement of the class B-ALL and divides the objects in BCR-ABL, E2A-PBX1, “Hyperdiploid>50”, MLL, TEL-AML1, T-ALL and OTHERS. sg/GEDatasets/. Moreover, we selected the genes that best define each group (40 per group), identified by Yeoh et al. with the chi-square metric [3]. We also converted the attributes to the interval [0, 1]. All this were made to use the data in the same way as in its original paper. For all datasets, we generate the initial population with the algorithms kmeans (KM), average-link (AL), single-link (SL) [10] and Shared Nearest Neighbors (SNN) [19].

Pevzner and Sze [7], studied a precise combinatorial formulation of this problem, called the planted motif problem, which is of particular interest because it is a challenging model for commonly used motif-finding algorithms [15]. In this work, we analyze two different encoding schemes for genetic algorithms to solve the planted motif finding problem. One representation encodes the initial position for the motif occurrences at each sequence, and the other encodes a candidate motif. We test the performance of both algorithms on a set of planted motif instances.

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