Flower Pollination Algorithm With Profile Technique For Multiple Sequence Alignment

Hussein, Ahmad MohdAziz; Abdullah, Rosni; AbdulRashid, Nuraini

doi:10.1109/jeeit.2019.8717513

Cited by 13 publications

(6 citation statements)

References 39 publications

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“…[91] The Flower Pollination Algorithm An algorithm that mimics the natural process of pollination in blooming plants, in which pollen is spread from one flower to another and fertilizes the plants. [92,93] Artificial Bee Colony…”

Section: Simulated Annealingmentioning

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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Section: Simulated Annealingmentioning

confidence: 99%

“…Ultimately, the Multiple Sequence Alignment is obtained by applying this approach. Another bioinspired algorithm applicable to MSA is the Flower Pollination algorithm (FPA) [92,93]. It is based on the mechanisms of self-pollination and cross-pollination that occur during flower pollination, among others [103].…”

Section: Other Bioinspired Techniquesmentioning

confidence: 99%

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

Ibrahim,

Yusof,

Eisa

et al. 2024

Applied Sciences

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“…Another ABC based three level multi objective approach for ribonucleic acid (RNA) multiple structural alignment problem [19]. A new kind approach based on flower pollination algorithm (FPA) [20] is proposed for solving MSA problem, this metaheuristic approach used for finding the alignment score based maximizing the sum of pairs and column score.…”

Section: Introductionmentioning

confidence: 99%

Solving multiple sequence alignment problems by using a swarm intelligent optimization based approach

Paruchuri

Kancharla²,

Dara³

2023

IJECE

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<span lang="EN-US">In this article, the alignment of multiple sequences is examined through swarm intelligence based an improved particle swarm optimization (PSO). A random heuristic technique for solving discrete optimization problems and realistic estimation was recently discovered in PSO. The PSO approach is a nature-inspired technique based on intelligence and swarm movement. Thus, each solution is encoded as “chromosomes” in the genetic algorithm (GA). Based on the optimization of the objective function, the fitness function is designed to maximize the suitable components of the sequence and reduce the unsuitable components of the sequence. The availability of a public benchmark data set such as the Bali base is seen as an assessment of the proposed system performance, with the potential for PSO to reveal problems in adapting to better performance. This proposed system is compared with few existing approaches such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) alignment (DIALIGN), PILEUP8, hidden Markov model training (HMMT), rubber band technique-genetic algorithm (RBT-GA) and ML-PIMA. In many cases, the experimental results are well implemented in the proposed system compared to other existing approaches.</span>

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“…In addition, some efforts have been made to apply the heuristics algorithm in 2019. Hussein et al (2019) used the flower pollination algorithm to solve the MSA. In 2020, Chowdhury and Garai proposed an alignment method based on a genetic algorithm–BSAGA ( Chowdhury and Garai 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding

Liu,

Yuan,

Zhang

et al. 2023

Bioinformatics

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Motivation Multiple sequence alignments (MSA) is one of the hotspots of current research and is commonly used in sequence analysis scenarios. However, there is no lasting solution for MSA because it is an NP-complete (Nondeterministic Polynomially) problem, and the existing methods still have room to improve the accuracy. Result We propose DPAMSA (Deep reinforcement learning with Positional encoding and self-Attention for MSA), based on deep reinforcement learning (DRL), to enhance the accuracy of the alignment Specifically, inspired by the translation technique in natural language processing (NLP), we introduce self-attention and positional encoding to improve accuracy and reliability. Firstly, positional encoding encodes the position of the sequence to prevent the loss of nucleotide position information. Secondly, the self-attention model is used to extract the key features of the sequence. Then input the features into a multi-layer perceptron, which can calculate the insertion position of the gap according to the features. In addition, a novel reinforcement learning environment is designed to convert the classic progressive alignment into progressive column alignment, gradually generating each column’s sub-alignment. Finally, merge the sub-alignment into the complete alignment. Extensive experiments based on several datasets validate our method’s effectiveness for MSA, outperforming some state-of-the-art methods in terms of the Sum-of-pairs and Column scores. Availability and implementation The process is implemented in Python and available as open-source software from https://github.com/ZhangLab312/DPAMSA. Supplementary information Supplementary data are available at Bioinformatics online.

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Flower Pollination Algorithm With Profile Technique For Multiple Sequence Alignment

Cited by 13 publications

References 39 publications

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

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

Solving multiple sequence alignment problems by using a swarm intelligent optimization based approach

Multiple sequence alignment based on deep reinforcement learning with self-attention and positional encoding

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