Hybrid multiobjective artificial bee colony for multiple sequence alignment

Rubio-Largo, Álvaro; Vega‐Rodríguez, Miguel A.; González-Álvarez, David L.

doi:10.1016/j.asoc.2015.12.034

Cited by 37 publications

(19 citation statements)

References 35 publications

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“…Rubio-Largo and et al [15] used Hybrid multi-objective artificial bee colony toward the MSA problem. They considered two objective functions for maintaining the consistency and the quality of the alignments, the number of totally conserved columns score and weighted sum-of-pairs function.…”

Section: Related Workmentioning

confidence: 99%

Using deep reinforcement learning approach for solving the multiple sequence alignment problem

2019

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In the present paper, we use a deep reinforcement learning (DRL) approach for solving the multiple sequence alignment problem which is an NP-complete problem. Multiple Sequence Alignment problem simply refers to the process of arranging initial sequences of DNA, RNA or proteins in order to maximize their regions of similarity. Multiple Sequence Alignment is the first step in solving many bioinformatics problems such as constructing phylogenetic trees. In this study, our proposed approach models the Multiple Sequence Alignment problem as a DRL problem and utilizes long short-term memory networks for estimation phase in the reinforcement learning algorithm. Furthermore, the actor-critic algorithm with experience-replay method is used for much quicker convergence process. Using deep Q-learning (an RL approach) and Q-network overcomes the complexity of other approaches. The experimental evaluation is performed on 8 different real-life datasets and in every used dataset our approach outperforms other well-known approaches and tools such as MAFFT, ClustalW, and other heuristic approaches in case of scoring in solving the MSA problem.

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Section: Related Workmentioning

confidence: 99%

Using deep reinforcement learning approach for solving the multiple sequence alignment problem

2019

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“…Evolutionary algorithms are also applied as a component for solving the multiple sequence alignment problems. The methods which illustrate the evolutionary algorithms are Particle Swarm Optimization (PSO) 27 , Ant colony optimization (ACO) 28 , Artificial Bee Colony (ABC) 29 , 30 , M-BPSO 31 , FTLPSO 32 and Genetic algorithm with Ant Colony Optimization (GA-ACO) 33 . Most of the MSA methods are prepared utilizing a single objective to align the protein/DNA sequences.…”

Section: Background Studymentioning

confidence: 99%

Bacterial Foraging Optimization –Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives

Manikandan

Ramyachitra

2017

Sci Rep

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This research work focus on the multiple sequence alignment, as developing an exact multiple sequence alignment for different protein sequences is a difficult computational task. In this research, a hybrid algorithm named Bacterial Foraging Optimization-Genetic Algorithm (BFO-GA) algorithm is aimed to improve the multi-objectives and carrying out measures of multiple sequence alignment. The proposed algorithm employs multi-objectives such as variable gap penalty minimization, maximization of similarity and non-gap percentage. The proposed BFO-GA algorithm is measured with various MSA methods such as T-Coffee, Clustal Omega, Muscle, K-Align, MAFFT, GA, ACO, ABC and PSO. The experiments were taken on four benchmark datasets such as BAliBASE 3.0, Prefab 4.0, SABmark 1.65 and Oxbench 1.3 databases and the outcomes prove that the proposed BFO-GA algorithm obtains better statistical significance results as compared with the other well-known methods. This research study also evaluates the practicability of the alignments of BFO-GA by applying the optimal sequence to predict the phylogenetic tree by using ClustalW2 Phylogeny tool and compare with the existing algorithms by using the Robinson-Foulds (RF) distance performance metric. Lastly, the statistical implication of the proposed algorithm is computed by using the Wilcoxon Matched-Pair Signed-Rank test and also it infers better results.In Bioinformatics, the sequence alignments are used to show evolutionary relationships by constructing phylogenetic trees. Sequence alignment and phylogenetic analysis are strongly related due to measuring the relatedness of homologous sequence. Generally the protein sequence consists of amino acids, which are linked with each other. Sequence alignment describes the mode of arrangement of protein sequence, in order to distinguish the areas of similarity among them 1 . Aligning refers to matching as many characters as possible from each sequence. Primarily, the sequence alignment is applied to infer functional, morphological and evolutionary relationship between the protein sequences. The alignment of the sequence is used to find similarity level between the query sequence and different database sequences.Today, there are several sequence alignment techniques are available and this research study concentrates on the multiple sequence alignment. One of the fundamental problems in computational biology is the alignment of multiple sequences of DNA/Protein. The computational approaches which are used to align the Protein/ DNA sequences generally falls into two categories: global and local alignments 2 . The multiple sequence alignment comes under the category of global alignment and it's an adjacent of pairwise alignment to incorporate more than two sequences at a time. Various methods have been implemented on MSA, but these approaches add up under three major classes such as: dynamic programming, Progressive and Iterative methods. In this research work the proposed BFO-GA algorithm comes under the category of Iterative -Progressive method...

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“…More recently, Rubio-Largo et al [23] proposed two new techniques for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm and the hybrid multi-objective Memetic Metaheuristic H4MSA algorithm [22]. These two algorithms considered the weighted sum-of-pairs function with affine gap penalties and the number of totally conserved columns score, and they were hybridized with the progressive and fast methodology Kalign [16].…”

Section: Related Workmentioning

confidence: 99%

“…To overcome these drawbacks, we have implemented a fast and low-memory cost codification of the alignments based on groups of gaps, similar to the one proposed by [23]. This MSA representation only stores the positions (begin, end) of the groups of gaps into the sequences, as follows.…”

Section: Msa Encodingmentioning

confidence: 99%

Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

et al. 2017

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Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a threeobjective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap symbols. The two last objectives introduce many plateaus in the search space, thus increasing the complexity of the problem. By taking as benchmark the BAliBASE data set, we carry out a rigorous comparative study by using four multi-objective metaheuristics, including the classical NSGA-II evolutionary algorithm and the more recent ones

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Hybrid multiobjective artificial bee colony for multiple sequence alignment

Cited by 37 publications

References 35 publications

Using deep reinforcement learning approach for solving the multiple sequence alignment problem

Using deep reinforcement learning approach for solving the multiple sequence alignment problem

Bacterial Foraging Optimization –Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives

Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

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