Uma heurística GRASP para o Problema da Sequência mais Próxima

O Problema da Sequência mais Próxima (PSMP) é um problema da Biologia Molecular que aparece no contexto da comparação de sequências. O objetivo é encontrar uma sequência que apresente a menor distância entre todas as sequências de um conjunto dado. O problema foi provado ser NP-difícil. Diversos algoritmos aproximativos, exatos e heurísticos tem sido propostos. Neste trabalho é proposto um algoritmo para o PSMP baseado na metaheurística GRASP, que apresentou soluções de boa qualidade em baixo tempo de execução nos testes realizados.Sociedad Argentina de Informática e Investigación Operativ

Uma heurística GRASP para o Problema da Sequência mais Próxima

O Problema da Sequência mais Próxima (PSMP) é um problema da Biologia Molecular que aparece no contexto da comparação de sequências. O objetivo é encontrar uma sequência que apresente a menor distância entre todas as sequências de um conjunto dado. O problema foi provado ser NP-difícil. Diversos algoritmos aproximativos, exatos e heurísticos tem sido propostos. Neste trabalho é proposto um algoritmo para o PSMP baseado na metaheurística GRASP, que apresentou soluções de boa qualidade em baixo tempo de execução nos testes realizados.Sociedad Argentina de Informática e Investigación Operativ

An Efficient Rank Based Approach for Closest String and Closest Substring

This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results

A list of parameterized problems in bioinformatics

In this report we present a list of problems that originated in bionformatics. Our aim is to collect information on such problems that have been analyzed from the point of view of Parameterized Complexity. For every problem we give its definition and biological motivation together with known complexity results.Postprint (published version

Parameterized Enumeration of Neighbour Strings and Kemeny Aggregations

In this thesis, we consider approaches to enumeration problems in the parameterized complexity setting. We obtain competitive parameterized algorithms to enumerate all, as well as several of, the solutions for two related problems Neighbour String and Kemeny Rank Aggregation. In both problems, the goal is to find a solution that is as close as possible to a set of inputs (strings and total orders, respectively) according to some distance measure. We also introduce a notion of enumerative kernels for which there is a bijection between solutions to the original instance and solutions to the kernel, and provide such a kernel for Kemeny Rank Aggregation, improving a previous kernel for the problem. We demonstrate how several of the algorithms and notions discussed in this thesis are extensible to a group of parameterized problems, improving published results for some other problems

Banishing Bias from Consensus Sequences

4SpringernonenoneBEN DOR A.; LANCIA G; PERONE J.; RAVI R.BEN DOR, A.; Lancia, Giuseppe; Perone, J.; Ravi, R

Banishing Bias from Consensus Sequences

. With the exploding size of genome databases, it is becoming increasingly important to devise search procedures that extract relevant information from them. One such procedure is particularly effective in finding new, distant members of a given family of related sequences: start with a multiple alignment of the given members of the family and use an integral or fractional consensus sequence derived from the alignment to further probe the database. However, the multiple alignment constructed to begin with may be biased due to skew in the sample of sequences used to construct it. We suggest strategies to overcome the problem of bias in building consensus sequences. When the intention is to build a fractional consensus sequence (often termed a profile), we propose assigning weights to the sequences such that the resulting fractional sequence has roughly the same similarity score against each of the sequences in the family. We call such fractional consensus sequences balanced profiles. On..

Banishing Bias from Consensus Sequences

. With the exploding size of genome databases, it is becoming increasingly important to devise search procedures that extract relevant information from them. One such procedure is particularly effective in finding new, distant members of a given family of related sequences: start with a multiple alignment of the given members of the family and use an integral or fractional consensus sequence derived from the alignment to further probe the database. However, the multiple alignment constructed to begin with may be biased due to skew in the sample of sequences used to construct it. We suggest strategies to overcome the problem of bias in building consensus sequences. When the intention is to build a fractional consensus sequence (often termed a profile), we propose assigning weights to the sequences such that the resulting fractional sequence has roughly the same similarity score against each of the sequences in the family. We call such fractional consensus sequences balanced profiles. On..