By Marie-France Sagot, Maria Emilia M.T. Walter
This publication 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 handle a large variety of present subject matters in computationl biology and bioinformatics that includes unique examine in laptop technology, arithmetic and facts in addition to in molecular biology, biochemistry, genetics, drugs, microbiology and different existence sciences.
Read or Download Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, PDF
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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,
The remainder of the paper is organized as follows. Section 2 states the problem we are going to solve and present a brief description of previous work on the subject. Section 3 describes the proposed algorithm along with the standard genetic algorithm. Section 4 discuss the experimental setup and presents the preliminary results. Section 5 presents the conclusions and some ideas for future research. 2 Problem Deﬁnition To deﬁne the Motif Finding Problem, we will start by deﬁning what a string is.
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1 Initialize P opSize individuals with random initial positions 2 Evaluate the fitness of the P opSize individuals and the best individual is taken as the super individual. 3 While the number of generations without changes in the fitness is less than M ax Iter N o Change, Do: 4 For i=1 to P opSize 5 Choose J individuals to participate in a match and the winner will be Parent1 6 Choose J individuals to participate in a match and the winner will be Parent2. 7 Perform crossover between Parent1 and Parent2 with probability Pc to obtain a new individual 8 Apply mutation to each new individual with probability Pm .