Search alternatives:
reaction optimization » production optimization (Expand Search), rational optimization (Expand Search), generation optimization (Expand Search)
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
based reaction » based action (Expand Search), based prediction (Expand Search), based detection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
reaction optimization » production optimization (Expand Search), rational optimization (Expand Search), generation optimization (Expand Search)
guided optimization » based optimization (Expand Search), model optimization (Expand Search)
based reaction » based action (Expand Search), based prediction (Expand Search), based detection (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
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The Pseudo-Code of the IRBMO Algorithm.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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IRBMO vs. meta-heuristic algorithms boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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IRBMO vs. feature selection algorithm boxplot.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Table1_A depth-first search algorithm for oligonucleotide design in gene assembly.DOCX
Published 2022“…Based on these fragments, a set of oligonucleotides for gene assembly is produced. …”
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Data Sheet 1_Clinical potential and experimental validation of prognostic genes in hepatocellular carcinoma revealed by risk modeling utilizing single cell and transcriptome constr...
Published 2025“…</p>Methods<p>The HCC datasets were obtained from public databases and then differential expression analysis were used to obtain significant gene expression profiles. Subsequently, univariate Cox regression analysis and PH assumption test were performed, and a risk model was developed using an optimal algorithm from 101 combinations on the TCGA-LIHC dataset to pinpoint prognostic genes. …”
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Data_Sheet_1_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.PDF
Published 2021“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”
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Data_Sheet_2_Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress.xls
Published 2021“…The expression stability of eight new RGs and commonly used RG 16s rRNA was assessed using geNorm, NormFinder, and BestKeeper algorithms. Moreover, the comprehensive analysis using the RefFinder program and the validation using target gene ctsR showed that dnaG and dnaN were the optimal multiple RGs for normalization at pH 4.0; ytvI, dnaG, and 16s rRNA at pH 3.5; icd and dnaG at pH 3.0; and ytvI, dnaG, and spoVE at pH 2.5. …”
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DataSheet_1_A novel prognostic model based on three integrin subunit genes-related signature for bladder cancer.docx
Published 2022“…This study endeavored to thoroughly analyze the utility of ITGs in BLCA through computer algorithm-based bioinformatics.</p>Methods<p>BLCA-related materials were sourced from reputable databases, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). …”
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Pseudo Code of RBMO.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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P-value on CEC-2017(Dim = 30).
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Memory storage behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Elite search behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Description of the datasets.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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S and V shaped transfer functions.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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S- and V-Type transfer function diagrams.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”
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Collaborative hunting behavior.
Published 2025“…In order to comprehensively verify the performance of IRBMO, this paper designs a series of experiments to compare it with nine mainstream binary optimization algorithms. The experiments are based on 12 medical datasets, and the results show that IRBMO achieves optimal overall performance in key metrics such as fitness value, classification accuracy and specificity. …”