Search alternatives:
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
path optimization » swarm optimization (Expand Search), whale optimization (Expand Search), based optimization (Expand Search)
based function » based functional (Expand Search), basis function (Expand Search), basis functions (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based path » based pa (Expand Search), based pathway (Expand Search)
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
path optimization » swarm optimization (Expand Search), whale optimization (Expand Search), based optimization (Expand Search)
based function » based functional (Expand Search), basis function (Expand Search), basis functions (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
based path » based pa (Expand Search), based pathway (Expand Search)
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141
Image 7_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec...
Published 2025“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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142
Image 1_Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in prec...
Published 2025“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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143
Image_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.TIFF
Published 2022“…The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. …”
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144
Table_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.XLSX
Published 2022“…The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. …”
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145
DataSheet_1_Machine learning-based on cytotoxic T lymphocyte evasion gene develops a novel signature to predict prognosis and immunotherapy responses for kidney renal clear cell ca...
Published 2023“…Therefore, we use machine learning algorithms to construct a signature based on cytotoxic T lymphocyte evasion genes (CTLEGs) to predict the immunotherapy responses of patients, so as to screen patients effective for immunotherapy.…”
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146
Table_1_Computational identification and experimental verification of a novel signature based on SARS-CoV-2–related genes for predicting prognosis, immune microenvironment and ther...
Published 2024“…We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. …”
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147
Table_1_Computational identification and experimental verification of a novel signature based on SARS-CoV-2–related genes for predicting prognosis, immune microenvironment and ther...
Published 2024“…We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. …”
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148
DataSheet_1_Computational identification and experimental verification of a novel signature based on SARS-CoV-2–related genes for predicting prognosis, immune microenvironment and...
Published 2024“…We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. …”
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149
DataSheet_1_Computational identification and experimental verification of a novel signature based on SARS-CoV-2–related genes for predicting prognosis, immune microenvironment and...
Published 2024“…We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. …”
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150
Streamlining signaling pathway reconstruction presentation
Published 2021“…Each individual method has its own input and output file formats, installation process, and user-specified parameters. Different algorithms employ varied objective functions and optimization strategies, and recognizing which method is appropriate for a particular dataset and how to set its unique parameters requires domain expertise in pathway reconstruction. …”
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151
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152
Table_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.xlsx
Published 2020“…Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. …”
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153
Image_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.tif
Published 2020“…Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. …”
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154
Table_3_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX
Published 2022“…Therefore, in this study, we employed a series of machine learning algorithms to computationally analyze the cell types of spleen and lymph based on single-cell CITE-seq sequencing data. …”
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155
Table_1_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX
Published 2022“…Therefore, in this study, we employed a series of machine learning algorithms to computationally analyze the cell types of spleen and lymph based on single-cell CITE-seq sequencing data. …”
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156
Table_2_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX
Published 2022“…Therefore, in this study, we employed a series of machine learning algorithms to computationally analyze the cell types of spleen and lymph based on single-cell CITE-seq sequencing data. …”
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