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Showing 41 - 60 results of 325 for search '(( genes based function optimization algorithm ) OR ( binary based cell optimization algorithm ))', query time: 0.73s Refine Results
  1. 41
    Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity

    Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity by George S. Watts (7962206)

    Published 2019
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  2. 42
    Phylogenetic history of genes 5 and 11 in three related genomes.

    Phylogenetic history of genes 5 and 11 in three related genomes. by Geleta Dugassa Barka (9099456)

    Published 2020
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  3. 43
    Schematic representation of semi-automated algorithm for organoids analyzing.

    Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

    Published 2025
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  4. 44
    Optimal Timings Codebook.xlsx

    Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

    Published 2021
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  5. 45
    Table_6_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx

    Table_6_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

    Published 2022
    “…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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  6. 46
    Table_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx

    Table_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

    Published 2022
    “…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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  7. 47
    Image_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.tif

    Image_1_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.tif by Ying Zhou (25031)

    Published 2022
    “…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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  8. 48
    Table_2_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx

    Table_2_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

    Published 2022
    “…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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  9. 49
    Table_4_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx

    Table_4_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

    Published 2022
    “…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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  10. 50
    Table_5_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx

    Table_5_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

    Published 2022
    “…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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  11. 51
    Table_3_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx

    Table_3_Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer.xlsx by Ying Zhou (25031)

    Published 2022
    “…</p>Methods<p>Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. …”
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  12. 52
    Table1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.xlsx

    Table1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.xlsx by Yu Yin (329063)

    Published 2023
    “…Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”
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  13. 53
    Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf

    Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf by Yu Yin (329063)

    Published 2023
    “…Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”
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  14. 54
    Table 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.xlsx

    Table 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.xlsx by Xiaopeng Zhan (4170574)

    Published 2025
    “…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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  15. 55
    Image 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf

    Image 1_Identification of a signature gene set for oxaliplatin sensitivity prediction in colorectal cancer.pdf by Xiaopeng Zhan (4170574)

    Published 2025
    “…Machine learning algorithms to these datasets was applied to identify genes associated with oxaliplatin response. …”
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  16. 56
    Table2_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table2_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  17. 57
    Table3_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table3_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  18. 58
    Table1_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table1_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  19. 59
    Table5_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table5_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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  20. 60
    Table6_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table6_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX by Jianwei Li (135213)

    Published 2024
    “…These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. …”
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