Search alternatives:
method optimization » lead optimization (Expand Search), path optimization (Expand Search), feature optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead optimization (Expand Search)
based method » based methods (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
method optimization » lead optimization (Expand Search), path optimization (Expand Search), feature optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead optimization (Expand Search)
based method » based methods (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
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141
Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf
Published 2023“…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”
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142
Data_Sheet_1_Explainable artificial intelligence based on feature optimization for age at onset prediction of spinocerebellar ataxia type 3.pdf
Published 2022“…The performance of 4 feature optimization methods and 10 machine learning (ML) algorithms were compared, followed by building the XAI based on the SHapley Additive exPlanations (SHAP). …”
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143
<i>FS</i> index of KNN on the selected feature subset.
Published 2024“…</p><p>Methods</p><p>Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). …”
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144
<i>ACC</i> index of KNN on the selected feature subset.
Published 2024“…</p><p>Methods</p><p>Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). …”
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145
<i>FS</i> index of SVM on the selected feature subset.
Published 2024“…</p><p>Methods</p><p>Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). …”
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146
The flowchart of the IG-GPSO.
Published 2024“…</p><p>Methods</p><p>Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). …”
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147
<i>ACC</i> index of SVM on the selected feature subset.
Published 2024“…</p><p>Methods</p><p>Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). …”
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148
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149
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150
Table_6_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx
Published 2022“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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151
Table_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx
Published 2022“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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152
Image_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.tif
Published 2022“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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153
Table_2_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx
Published 2022“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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154
Table_4_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx
Published 2022“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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155
Table_5_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx
Published 2022“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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156
Table_3_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx
Published 2022“…Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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157
Image_2_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF
Published 2022“…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …”
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158
Image_1_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF
Published 2022“…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …”
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159
Image_3_A two-stage hybrid gene selection algorithm combined with machine learning models to predict the rupture status in intracranial aneurysms.TIF
Published 2022“…First, we used the Fast Correlation-Based Filter (FCBF) algorithm to filter a large number of irrelevant and redundant genes in the raw dataset, and then used the wrapper feature selection method based on the he Multi-layer Perceptron (MLP) neural network and the Particle Swarm Optimization (PSO), accuracy (ACC) and mean square error (MSE) were then used as the evaluation criteria. …”
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160