A new hybrid bio-inspired approach to resolve the multiple sequence alignment problem

Zemali, Elamine; Boukra, Abdelmadjid

doi:10.1109/codit.2016.7593544

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…[ 84,98] Bacterial Foraging Optimization A nature-inspired optimization algorithm that is inspired by the foraging of bacteria. [74,99] Genetic Algorithm (GA)…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

“…In this way, through processes known as migration and emigration, species travel between the regions. Zemali and Boukra [98] presented a novel hybrid method for performing MSA using an optimization algorithm based on biogeography. The method creates the initial population using Progressive Alignment, which means that a distinct set of parameters is utilized for each territory.…”

Section: Other Bioinspired Techniquesmentioning

confidence: 99%

“…The authors have assessed the suggested approach using the BAliBASE benchmark. To tackle the MSA problem, [98] combined earlier research with Talbi and Draa's Quantum Evolutionary approach [102]. Pairwise alignments and the hill climbing process construct the initial population.…”

Section: Other Bioinspired Techniquesmentioning

confidence: 99%

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Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

Ibrahim,

Yusof,

Eisa

et al. 2024

Applied Sciences

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Multiple Sequence Alignment (MSA) plays a pivotal role in bioinformatics, facilitating various critical biological analyses, including the prediction of unknown protein structures and functions. While numerous methods are available for MSA, bioinspired algorithms stand out for their efficiency. Despite the growing research interest in addressing the MSA challenge, only a handful of comprehensive reviews have been undertaken in this domain. To bridge this gap, this study conducts a thorough analysis of bioinspired-based methods for MSA through a systematic literature review (SLR). By focusing on publications from 2010 to 2024, we aim to offer the most current insights into this field. Through rigorous eligibility criteria and quality standards, we identified 45 relevant papers for review. Our analysis predominantly concentrates on bioinspired-based techniques within the context of MSA. Notably, our findings highlight Genetic Algorithm and Memetic Optimization as the most commonly utilized algorithms for MSA. Furthermore, benchmark datasets such as BAliBASE and SABmark are frequently employed in evaluating MSA solutions. Structural-based methods emerge as the preferred approach for assessing MSA solutions, as revealed by our systematic literature review. Additionally, this study explores current trends, challenges, and unresolved issues in the realm of bioinspired algorithms for MSA, offering practitioners and researchers valuable insights and comprehensive understanding of the field.

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“…[ 84,98] Bacterial Foraging Optimization A nature-inspired optimization algorithm that is inspired by the foraging of bacteria. [74,99] Genetic Algorithm (GA)…”

Section: Biogeography-based Optimizationmentioning

confidence: 99%

Section: Other Bioinspired Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

Ibrahim,

Yusof,

Eisa

et al. 2024

Applied Sciences

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“…Dynamic programming extends its utility to multiple sequence alignment algorithms, such as the progressive and iterative methods [8,9]. Aligning N sequences using dynamic programming is an NP-Hard problem [10] that stems from the complexity of considering all possible combinations and alignments among the N sequences. To address complexity challenges in MSA, heuristic methods [11] and approximation algorithms [12] are employed in practice for the MSA of a large number of sequences.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Multiple Sequence Alignments Using Parallel Computing

Bani Baker,

Al-Hussien,

Al-Ayyoub

2024

Computation

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Multiple sequence alignment (MSA) stands as a critical tool for understanding the evolutionary and functional relationships among biological sequences. Obtaining an exact solution for MSA, termed exact-MSA, is a significant challenge due to the combinatorial nature of the problem. Using the dynamic programming technique to solve MSA is recognized as a highly computationally complex algorithm. To cope with the computational demands of MSA, parallel computing offers the potential for significant speedup in MSA. In this study, we investigated the utilization of parallelization to solve the exact-MSA using three proposed novel approaches. In these approaches, we used multi-threading techniques to improve the performance of the dynamic programming algorithms in solving the exact-MSA. We developed and employed three parallel approaches, named diagonal traversing, blocking, and slicing, to improve MSA performance. The proposed method accelerated the exact-MSA algorithm by around 4×. The suggested approaches could be basic approaches to be combined with many existing techniques. These proposed approaches could serve as foundational elements, offering potential integration with existing techniques for comprehensive MSA enhancement.

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WOAMSA: Whale Optimization Algorithm for Multiple Sequence Alignment of Protein Sequence

Kumar¹,

Kumar²,

Nidhya³

2020

Computational Vision and Bio-Inspired Computing

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A new hybrid bio-inspired approach to resolve the multiple sequence alignment problem

Cited by 5 publications

References 22 publications

Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

Bioinspired Algorithms for Multiple Sequence Alignment: A Systematic Review and Roadmap

Accelerating Multiple Sequence Alignments Using Parallel Computing

WOAMSA: Whale Optimization Algorithm for Multiple Sequence Alignment of Protein Sequence

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