On the role of metaheuristic optimization in bioinformatics

Calvet, Laura; Benito, Sergio; Juan, Ángel A.; Carrasco, Ferran Prados

doi:10.1111/itor.13164

Cited by 9 publications

(6 citation statements)

References 219 publications

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“…In the subsequent step, which involves modifying the initial population, fundamental operators are utilized for successive generations, aiming to reach the global optimum [29]. Given the random initiation in the evolutionary technique for Multiple Sequence Alignment (MSA), the Evolutionary Algorithm for Sequences (EAS) has taken additional steps to enhance similarities in sequence alignment [9]. Evolutionary computation provides intriguing approaches for multisequence alignment [21], resulting in higher alignment accuracy during the MSA runtime process.…”

Section: Evolutionary Methodsmentioning

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: Evolutionary Methodsmentioning

confidence: 99%

“…These algorithms mimic natural processes or use population-based strategies, which help them find optimal solutions and avoid becoming stuck in local optima. Due to their adaptability and flexibility, these algorithms are well-suited for addressing the challenges and complexities of the MSA problem [9].…”

Section: Introductionmentioning

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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“…Bioinformatics is an interdisciplinary field that combines biology, computer science, and statistics to analyze and interpret biological behaviour [ 1 ]. It identifies and diagnoses cancer by examining gene activity and cellular function.…”

Section: Introductionmentioning

confidence: 99%

Fractal feature selection model for enhancing high-dimensional biological problems

Alsaeedi,

Al-Mahmood,

Alnaseri

et al. 2024

BMC Bioinformatics

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The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%.

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“…Several studies have demonstrated the effectiveness of optimization and heuristic methods such as Bayesian optimizer, grid search, and random search methods in improving model prediction. Calvet et al [ 23 ] demonstrated the effect of optimization algorithms in solving bioinformatics problems such as molecular docking and protein structure prediction. They suggested the combination of multiple heuristic and optimization algorithms to solve modern computational problems.…”

Section: Introductionmentioning

confidence: 99%

Role of Optimization in RNA–Protein-Binding Prediction

Alsenan,

Al-Turaiki,

Aldayel

et al. 2024

CIMB

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RNA-binding proteins (RBPs) play an important role in regulating biological processes, such as gene regulation. Understanding their behaviors, for example, their binding site, can be helpful in understanding RBP-related diseases. Studies have focused on predicting RNA binding by means of machine learning algorithms including deep convolutional neural network models. One of the integral parts of modeling deep learning is achieving optimal hyperparameter tuning and minimizing a loss function using optimization algorithms. In this paper, we investigate the role of optimization in the RBP classification problem using the CLIP-Seq 21 dataset. Three optimization methods are employed on the RNA–protein binding CNN prediction model; namely, grid search, random search, and Bayesian optimizer. The empirical results show an AUC of 94.42%, 93.78%, 93.23% and 92.68% on the ELAVL1C, ELAVL1B, ELAVL1A, and HNRNPC datasets, respectively, and a mean AUC of 85.30 on 24 datasets. This paper’s findings provide evidence on the role of optimizers in improving the performance of RNA–protein binding prediction.

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On the role of metaheuristic optimization in bioinformatics

Cited by 9 publications

References 219 publications

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

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

Fractal feature selection model for enhancing high-dimensional biological problems

Role of Optimization in RNA–Protein-Binding Prediction

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