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Showing 141 - 160 results of 301 for search '(( gene based function optimization algorithm ) OR ( binary based art optimization algorithm ))', query time: 0.79s Refine Results
  1. 141
    Identification and validation of hub DE-podos in bulk RNA-seq.

    Identification and validation of hub DE-podos in bulk RNA-seq. by Hailin Li (453832)

    Published 2025
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  2. 142
    Table_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.xlsx

    Table_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.xlsx by Yao Gao (1420339)

    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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  3. 143
    Image_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.tif

    Image_1_A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.tif by Yao Gao (1420339)

    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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  4. 144
    Table_3_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX

    Table_3_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX by Hao Li (31608)

    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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  5. 145
    Table_1_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX

    Table_1_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX by Hao Li (31608)

    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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  6. 146
    Table_2_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX

    Table_2_Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods.XLSX by Hao Li (31608)

    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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  7. 147
    Immune microenvironment landscape in HCC patients with different risk score.

    Immune microenvironment landscape in HCC patients with different risk score. by Qian Feng (302178)

    Published 2023
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  8. 148
    Data Sheet 1_Multi-omics analysis and metastasis risk factor prediction in N1b stage PTMC: insights into immune infiltration and therapeutic implications.docx

    Data Sheet 1_Multi-omics analysis and metastasis risk factor prediction in N1b stage PTMC: insights into immune infiltration and therapeutic implications.docx by Hao Dai (4277254)

    Published 2025
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  9. 149
    Changes in glutamatergic neurons (RiboTag::vGluT2-Cre).

    Changes in glutamatergic neurons (RiboTag::vGluT2-Cre). by Lech Kaczmarczyk (587512)

    Published 2022
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  10. 150
    Changes in GABA neurons (RiboTag::Gad2-Cre).

    Changes in GABA neurons (RiboTag::Gad2-Cre). by Lech Kaczmarczyk (587512)

    Published 2022
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  11. 151
    Changes in astrocytes (RiboTag::Cx43-Cre).

    Changes in astrocytes (RiboTag::Cx43-Cre). by Lech Kaczmarczyk (587512)

    Published 2022
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  12. 152
    Changed differential alternative splicing events (DASEs) patterns between PCOS and Normal granulosa cells.

    Changed differential alternative splicing events (DASEs) patterns between PCOS and Normal granulosa cells. by Linlin Yang (737661)

    Published 2024
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  13. 153
    Table_2_A novel prognostic signature related to programmed cell death in osteosarcoma.xlsx

    Table_2_A novel prognostic signature related to programmed cell death in osteosarcoma.xlsx by Yu-Chen Jiang (11264151)

    Published 2024
    “…This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.…”
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  14. 154
    Table_1_A novel prognostic signature related to programmed cell death in osteosarcoma.xlsx

    Table_1_A novel prognostic signature related to programmed cell death in osteosarcoma.xlsx by Yu-Chen Jiang (11264151)

    Published 2024
    “…This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.…”
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  15. 155
    Table4_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls

    Table4_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls by Wenhao An (19366816)

    Published 2024
    “…LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. …”
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  16. 156
    Table2_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls

    Table2_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls by Wenhao An (19366816)

    Published 2024
    “…LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. …”
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  17. 157
    Table3_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls

    Table3_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls by Wenhao An (19366816)

    Published 2024
    “…LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. …”
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  18. 158
    Table1_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls

    Table1_Identification of crosstalk genes and immune characteristics between Alzheimer’s disease and atherosclerosis.xls by Wenhao An (19366816)

    Published 2024
    “…LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. …”
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  19. 159
    An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach

    An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach by Sanaa Badr (20628838)

    Published 2025
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  20. 160
    Table2_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.XLSX

    Table2_Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.XLSX by Yaxian Song (10454804)

    Published 2022
    “…Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. …”
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