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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Extra resources for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings
1. They were designed to contain at least two distinguishing structures. These structures are heterogeneous and are in diﬀerent reﬁnement levels. 40 K. F. P. de Souto Table 1. Datasets characteristics Dataset n d nE K E1 K E2 K E3 K E4 ds2c2sc13 588 2 3 2 5 13 - ds3c3sc6 905 2 2 3 6 - - ds4c2sc8 485 2 2 2 8 - - 72 3571 4 2 3 4 2 leukemia 327 271 2 3 7 - - golub Fig. 1. Artiﬁcial datasets For the real datasets, the diﬀerent structures correspond to diﬀerent known classiﬁcations of the data. Thus, we assume that the known classiﬁcations are in accordance with some of the clustering criteria we use.
Genome Res. 15, 1202–1215 (1998) 4. : Finding Motifs Using Random Projections. Journal of Computational Biology 9(2), 225–242 (2002) 5. : MDGA: Motif Discovery Using A Genetic Algorithm. GECCO’05 (June 25-29, 2005) 6. : Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989) 7. : Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biolofy. Cambridge University Press, Cambridge (1997) 8. : Identifying DNA and protein patterns with statistically signiﬁcant alignments of multiple sequences.
The position based algorithm was not able to ﬁnd the optimal solution in neither of the 30 executions. In the second instance we can see similar results as in the ﬁrst one. 30 G. A. Brizuela Table 2. D. D. 52 Table 3. Comparison of average total score (F ) for SGA allowed extra time, the PbGA, and the Gibbs sampler. The score in parenthesis is the best score found by the algorithms in 30 runs, and the ones in bold are the score of the best solution found. 13(613) 613 Table 4. Comparison of the performance achieved by MbGA on a real instance.
Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings by K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de Souza (auth.), Marie-France Sagot, Maria Emilia M. T. Walter (eds.)