RETRACTED ARTICLE: Improved swarm intelligence algorithm for protein folding prediction

Wang, Shigang

doi:10.1007/s10586-018-2257-1

Cited by 3 publications

(2 citation statements)

References 11 publications

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“… Swarm Optimization Techniques Nature-inspired algorithms come in two varieties: bio-inspired algorithms and physics/chemistryinspired algorithms [15]. Evolutionary computation and swarm intelligence (SI) are two examples of bio-inspired algorithms.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparative Study on Feature Selection Methods for Protein

Alkady

El-Bahnasy

Gad

2022

IJICIS

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The automated and high-throughput identification of protein function is one of the main issues in computational biology. Predicting the protein's structure is a crucial step in this procedure. In recent years, a wide range of approaches for predicting protein structure has been put forth. They can be divided into two groups: database-based and sequence-based. The first is to identify the principles behind protein structure and attempts to extract valuable characteristics from amino acid sequences. The second one uses pre-existing public annotation databases for data mining. This study emphasizes the sequence-based method and makes use of the ability of amino acid sequences to predict protein activity. The amino acid composition approach, the amino acid tuple approach, and several optimization algorithms were compared. Different protein sequence data sets were used in our experiments. Five classifiers were tested in this research. The best accuracy is 98% using across 10fold cross-validation. This represents the highest performance in the Human dataset.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Comparative Study on Feature Selection Methods for Protein

Alkady

El-Bahnasy

Gad

2022

IJICIS

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“…Since deep insight into 3D protein structures is a key for understanding molecular mechanisms of many civilization diseases and for the production of effective drugs, structural genomics tries to determine and describe the 3D structure of every protein that is encoded by a given sequenced genome. This is done by combining traditional experimental methods, like X-ray crystallography or Nuclear Magnetic Resonance (NMR), with computational modeling approaches that use various prediction methods for structure determination [18,54,76,80].…”

Section: Introductionmentioning

confidence: 99%

Spark-IDPP: high-throughput and scalable prediction of intrinsically disordered protein regions with Spark clusters on the Cloud

2018

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Intrinsically disorder proteins (IDPs) constitute a significant part of proteins that exist and act in cells of living organisms. IDPs play key roles in central cellular processes and some of them are closely related to various human diseases, like cancer or neurodegenerative disorders. Identification of IDPs and studying their structural characteristics have become an important part of structural bioinformatics and structural genomics. However, growing amount of genomic and protein sequences in public repositories pose a pressure on existing methods for identification of IDPs. Large volumes of protein amino acid sequences need to be analyzed in terms of propensity to form disordered regions, and this task requires novel tools and scalable platforms to cope with this big biological data challenge. In this paper, we show how the identification of disordered regions of 3D protein structures can be efficiently accelerated with the use of Apache Spark cluster established and scaled on the public Cloud. For this purpose, we propose Spark-based meta-predictor (Spark-IDPP), which enables efficient prediction of disordered regions of proteins on a large-scale. Results of our performance tests show that, for large data sets, our method achieves almost linear speedup, when scaling out the computations on the 32-node Spark cluster located in the Azure cloud. This proves that through appropriate partitioning of data and by increasing the degree of parallelism, we can significantly improve efficiency of IDP predictions. Additionally, by using several basic predictors, aggregating their ranks in various consensus modes, and filtering the final outcome with a dedicated fuzzy filter, the Spark-IDPP increases the quality of predictions.

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A sparrow search algorithm with intelligent weight factors and mutation operators and applications to image classification

Yue

Gao

et al. 2022

IFS

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The surface inspection of strip steel defects plays a vital role in the industry, and it has attracted widespread attention in the industry. In this paper, an improved sparrow search algorithm (WMR-SSA) with intelligent weighting factors and mutation operators is proposed, WMR-SSA can balance the development capability of the algorithm based on the number of iterations. In addition, WMR-SSA enhances the local search capability of the algorithm through mutation operators. At the same time, the algorithm determines the initial position of the population by random walk to enhance the diversity of the population. The WMR-SSA algorithm is compared with GA, PSO, CS, GWO, BSA, and original SSA, and the experiment proves that the WMR-SSA algorithm is better than other algorithms. In this study, WMR-SSA is combined with BP neural network and implemented for the classification of defective strip images. The accuracy and stability of WMR-SSA-BP are effectively demonstrated experimentally by comparing it with classifiers optimized by other intelligent algorithms.

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RETRACTED ARTICLE: Improved swarm intelligence algorithm for protein folding prediction

Cited by 3 publications

References 11 publications

Comparative Study on Feature Selection Methods for Protein

Comparative Study on Feature Selection Methods for Protein

Spark-IDPP: high-throughput and scalable prediction of intrinsically disordered protein regions with Spark clusters on the Cloud

A sparrow search algorithm with intelligent weight factors and mutation operators and applications to image classification

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