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Showing 41 - 60 results of 171 for search '(( gene based process optimization algorithm ) OR ( binary based smart optimization algorithm ))', query time: 1.25s Refine Results
  1. 41
    Summary of the BRIDES node selection procedure providing the minimum number of nodes required to reach a maximal score, and the number of multiple solutions with the same score for different scenarios and weighted model schemes.

    Summary of the BRIDES node selection procedure providing the minimum number of nodes required to reach a maximal score, and the number of multiple solutions with the same score for... by Cindy Bouchard (6844979)

    Published 2022
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  2. 42
    Table_1_BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization.XLSX

    Table_1_BLVector: Fast BLAST-Like Algorithm for Manycore CPU With Vectorization.XLSX by Sergio Gálvez (548022)

    Published 2021
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  3. 43
    Data_Sheet_1_CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage.docx

    Data_Sheet_1_CLGBO: An Algorithm for Constructing Highly Robust Coding Sets for DNA Storage.docx by Yanfen Zheng (3814507)

    Published 2021
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  4. 44
    SV detection workflow.

    SV detection workflow. by De-Min Duan (20685513)

    Published 2025
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  5. 45
    The overview of the proposed method.

    The overview of the proposed method. by Sepideh Zununi Vahed (9861298)

    Published 2024
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  6. 46
    GSE96058 information.

    GSE96058 information. by Sepideh Zununi Vahed (9861298)

    Published 2024
    “…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”
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  7. 47
    The performance of classifiers.

    The performance of classifiers. by Sepideh Zununi Vahed (9861298)

    Published 2024
    “…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …”
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  8. 48
    Optimized combination methods for exploring novel space environment-responsive genes and their roles: insights from space-flown <i>C. elegans</i> and their implications for astronauts

    Optimized combination methods for exploring novel space environment-responsive genes and their roles: insights from space-flown <i>C. elegans</i> and their implications for astrona... by Zejun Li (176523)

    Published 2025
    “…</p> <p>We employed an optimized combination algorithm that integrated two co-expression network analysis methods and four machine learning-based models to identify space environment-responsive genes (SEGs) in space-flown <i>C. elegans</i>. …”
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  9. 49
    Simultaneous Estimation of Many Sparse Networks via Hierarchical Poisson Log-Normal Model

    Simultaneous Estimation of Many Sparse Networks via Hierarchical Poisson Log-Normal Model by Changhao Ge (22410399)

    Published 2025
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  10. 50
    Image1_mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.pdf

    Image1_mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.pdf by Guoliang Cheng (12458973)

    Published 2024
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  11. 51
    Table1_mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.xls

    Table1_mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.xls by Guoliang Cheng (12458973)

    Published 2024
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  12. 52
    Presentation1_A novel approach to analyze the association characteristics between post-spliced introns and their corresponding mRNA.zip

    Presentation1_A novel approach to analyze the association characteristics between post-spliced introns and their corresponding mRNA.zip by Suling Bo (11146299)

    Published 2023
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  13. 53
    Table_2_Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics.XLSX

    Table_2_Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics.XLSX by Zhandong Li (4284526)

    Published 2022
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  14. 54
    Table_1_Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics.XLSX

    Table_1_Identification of cortical interneuron cell markers in mouse embryos based on machine learning analysis of single-cell transcriptomics.XLSX by Zhandong Li (4284526)

    Published 2022
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  15. 55
    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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  16. 56
    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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  17. 57
    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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  18. 58
    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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  19. 59
    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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  20. 60
    Table4_MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion.XLSX

    Table4_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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