Search alternatives:
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead 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)
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead 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)
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Table 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.xlsx
Published 2025“…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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Image 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf
Published 2025“…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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Table1_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.XLSX
Published 2022“…Moreover, ssGSEA and GSEA algorithm reveled different immunological characteristics and biological function variation in different risk groups. …”
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Image2_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.TIFF
Published 2022“…Moreover, ssGSEA and GSEA algorithm reveled different immunological characteristics and biological function variation in different risk groups. …”
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Image1_Identification of Immune-Related Genes for Risk Stratification in Multiple Myeloma Based on Whole Bone Marrow Gene Expression Profiling.TIFF
Published 2022“…Moreover, ssGSEA and GSEA algorithm reveled different immunological characteristics and biological function variation in different risk groups. …”
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66
The brief description on the WTCCC dataset.
Published 2024“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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The penetrance tables for the 8 DNME models.
Published 2024“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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The penetrance tables for the 8 DME models.
Published 2024“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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The penetrance tables for the 6 DNME3 models.
Published 2024“…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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Data Sheet 4_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf
Published 2025“…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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71
Data Sheet 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf
Published 2025“…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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Data Sheet 2_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf
Published 2025“…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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73
Data Sheet 3_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf
Published 2025“…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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74
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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