An Efficient Ant Colony Algorithm for Multiple Sequences Alignment

Chen, Ling; Zou, Lingjun; Chen, Juan

doi:10.1109/icnc.2007.189

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…There is a class of optimization approaches to solving intractable problems, including those in NP-Complete. For the problem of multiple sequence alignment, several authors have considered optimization approaches, such as Simulated Annealing in [14] and [15], Particle Swarm Optimization in [16] and [17], Genetic Algorithm in [18] and [19], Tabu Search in [20] and [21], Ant Colony in [22] and [23], and Bee Colony in [24] and [25], among others. The most important reason for not using these methods to solve our alignment problem is that they search for local optimum solutions on the basis of several near-optimal solutions by further rounds of optimizing iterations, making them quite inefficient.…”

Section: Related Work a Multiple Sequence Alignmentmentioning

confidence: 99%

Discovery of MicroDependencies

Sun

2019

IEEE Access

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Data quality rules compose a class of frequently used tools whose purpose is to improve data quality: examples of these rules include functional dependences, conditional functional dependences, and editing rules, among others. Dependences with stronger expressivity can help to detect more data errors and impute more missing values. To the best of our knowledge, most existing rules consider each attribute as an inseparable whole. In many applications, however, a part of a value contains useful information, and more powerful rules can thus be formed to handle data quality problems. In this paper, we aim to discover the rules of this type, namely, microDependences. The left-hand side of a microDependence is a star-free regular expression, along with the positions of the partial information. This indicates that if a string-type attribute value matches the regular expression, elements at the specified positions can determine another attribute's value. To discover microDependences, strings with similar forms should be clustered together. Moreover, similar strings should be aligned vertically to shift elements with a similar meaning to the same position. Then, microDependences can be discovered directly or by the existing methods. Both the clustering and aligning tasks are challenging and play key roles in discovering microDependences. A greedy bottom-up framework is proposed to do the clustering and aligning work simultaneously. For efficiency, several pruning strategies are proposed to reduce the time consumed. In the experimental study, our methods' performances are verified on both real and synthetic data sets.INDEX TERMS Aligning, clustering, MicroDependencies, pruning strategies.

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Section: Related Work a Multiple Sequence Alignmentmentioning

confidence: 99%

Discovery of MicroDependencies

Sun

2019

IEEE Access

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“…These approaches are based on the improvement of the given initial alignment through a series of some iterations until a stopping criterion is reached. They include genetic algorithm (GA) [5], simulated annealing algorithm (SA) [6], particle swarm optimization (PSO) [7], GA-ACO algorithm [8], Ant Colony Algo-rithm [9] and so on. Generally, these metaheuristics are able to find nearly optimal solutions for large instances in a reasonable processing time.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Cooperative Method to Solve Multiple Sequence Alignment Problem

Chaabane

2018

Computational Intelligence and Its Applications

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In this research work, we propose a cooperative approach called simulated particle swarm optimization (SPSO) which is based on metaheuristics to find an approximate solution for the multiple sequence alignment (MSA) problem. The developed approach uses the particle swam optimization (PSO) algorithm to discover the search space globally and the simulated annealing (SA) technique to improve the population leader « gbest » quality in order to overcome local optimum problem. Simulation results on BaliBASE benchmarks have shown the potent of the proposed method to produce good quality alignments comparing to those given by other existing methods.

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“…For this reason, several metaheuristic methods have been designed to obtain suboptimal alignments. Metaheuristics have also been applied to solve this problem [8], for example, Ant Colony Algorithm [9], Simulated Annealing [10,11], Genetic Algorithms [12], among others. The disadvantage is that metaheuristics do not guarantee optimal solutions, but solutions generated can be very close to optimal solution in a reasonable processing time.…”

Section: Introductionmentioning

confidence: 99%

Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment

Liñán-García

Gallegos-Araiza

2012

ISRN Artificial Intelligence

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A new algorithm for solving sequence alignment problem is proposed, which is named SAPS (Simulated Annealing with Previous Solutions). This algorithm is based on the classical Simulated Annealing (SA). SAPS is implemented in order to obtain results of pair and multiple sequence alignment. SA is a simulation of heating and cooling of a metal to solve an optimization problem. In order to select randomly a current solution, SAPS algorithm chooses a solution from solutions that have been previously generated within the Metropolis Cycle. This simple change has led to increase the quality of the solution to the problem of aligning genomic sequences with respect to the classical Simulated Annealing algorithm. The parameters of SAPS, for certain instances, are tuned by an analytical method, and some parameters have experimentally been tuned. SAPS has generated high-quality results in comparison with the classical SA. The instances used are specific genes of the AIDS virus.

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An Efficient Ant Colony Algorithm for Multiple Sequences Alignment

Cited by 4 publications

References 23 publications

Discovery of MicroDependencies

Discovery of MicroDependencies

An Efficient Cooperative Method to Solve Multiple Sequence Alignment Problem

Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment

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