Identification of Essential Protein Using Chemical Reaction Optimization and Machine Learning Technique

Inzamam-Ul-Hossain, Md.; Islam, Md. Rafiqul

doi:10.1109/tcbb.2022.3233473

Cited by 3 publications

(4 citation statements)

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“…Hossain and Islam [24] introduced a method for identifying crucial proteins by employing a combination of a metaheuristic algorithm and a machine learning technique. They utilized three distinct classifiers within the objective function of their proposed strategy: LightGBM, Random Forest, and XGBoost.…”

Section: Light Gradient Boosting Machine (Lightgbm) Sub-techniquementioning

confidence: 99%

“…The surge in genomic and proteomic data demands innovative computational techniques to predict and interpret PPIs, thereby advancing avenues like drug discovery, disease modeling, and personalized medicine. Machine learning (ML), a branch of artificial intelligence, has emerged as a powerful tool in this context, showing remarkable proficiency in pattern recognition within complex data [7,24,25]. Using ML for PPI prediction is not just an exciting technological advance; it's a hopeful and promising area that integrates various biological data types to produce insights previously unattainable through conventional methods.…”

Section: Introductionmentioning

confidence: 99%

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Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Taha

2023

Preprint

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This survey paper provides an in-depth analysis of various machine learning techniques and algorithms that are utilized in the detection of PPI (Protein-Protein Interactions). For every technique examined, the paper evaluates its efficiency, shortcomings, possibilities for enhancement, and outlook for the future. A major challenge in current survey papers focusing on machine learning algorithms for PPI identification is the successful categorization of these algorithms. To overcome this challenge, the paper introduces a novel hierarchical taxonomy that organizes algorithms into more intricate categories and distinct techniques. The proposed taxonomy is constructed on a four-tier structure, beginning with the broad methodology category, and ending with specific sub-techniques. This structure facilitates a more systematic and exhaustive categorization of algorithms, aiding researchers in grasping the connections between different algorithms and techniques. Included in the paper are both empirical and experimental assessments to classify the various techniques. The empirical assessment judges the techniques according to four standards. The experimental evaluations carry out the following rankings: (1) the algorithms that employ the same specific sub-technique, (2) the different sub-techniques that employ the same technique, (3) the different techniques that employ the same methodology sub-category, and (4) the different methodology sub-categories within the same methodology category. By merging the new methodological taxonomy, empirical analyses, and experimental evaluations, the paper provides a multifaceted and thorough comprehension of the machine learning methods and algorithms for PPI detection. This synthesis helps researchers make well-informed decisions. In its conclusion, the paper furnishes crucial insights into the future possibilities of machine learning techniques for PPI identification, underscoring potential advancements and areas ripe for continued exploration and development

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Section: Light Gradient Boosting Machine (Lightgbm) Sub-techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Taha

2023

Preprint

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“…Likewise, the gradient-based gravitational search algorithm has been employed for conformational searches of the basic building blocks of proteins [29]. In reference [30], the identification of essential proteins using chemical reaction optimization and machine learning has been performed. Inspired by these possibilities, we propose an ingenious crow search algorithm (ICSA) to solve the aforementioned protein folding problem.…”

Section: Introductionmentioning

confidence: 99%

“…For the optimization environment, search agent no. (30), maximum iteration (500) and total no. of independent runs (20) are kept constant for all the algorithms.…”

mentioning

confidence: 99%

Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction

et al. 2023

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Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences.

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Solving maximum clique problem using chemical reaction optimization

2023

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Identification of Essential Protein Using Chemical Reaction Optimization and Machine Learning Technique

Cited by 3 publications

References 46 publications

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction

Solving maximum clique problem using chemical reaction optimization

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