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Showing 61 - 80 results of 188 for search '(( binary data guided optimization algorithm ) OR ( genes based processes optimization algorithm ))', query time: 0.41s Refine Results
  1. 61
    Friedman average rank sum test results.

    Friedman average rank sum test results. by Chenyi Zhu (9383370)

    Published 2025
    “…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”
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  2. 62
    SV detection workflow.

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

    Published 2025
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  3. 63
    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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  4. 64
    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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  5. 65
    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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  6. 66
    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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  7. 67
    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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  8. 68
    Summary of steps in iterative point set registration for scRNA-seq data.

    Summary of steps in iterative point set registration for scRNA-seq data. by Amir Alavi (9569337)

    Published 2020
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  9. 69
    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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  10. 70
    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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  11. 71
    Workflow overview to reconstruct integrated host-virus genome-scale models and detect promising compounds with an antiviral activity.

    Workflow overview to reconstruct integrated host-virus genome-scale models and detect promising compounds with an antiviral activity. by Nantia Leonidou (14833068)

    Published 2023
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  12. 72
    DataSheet_1_A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma.doc

    DataSheet_1_A Novel Radiogenomics Biomarker Based on Hypoxic-Gene Subset: Accurate Survival and Prognostic Prediction of Renal Clear Cell Carcinoma.doc by Jiahao Gao (7799222)

    Published 2021
    “…Purpose<p>To construct a novel radiogenomics biomarker based on hypoxic-gene subset for the accurate prognostic prediction of clear cell renal cell carcinoma (ccRCC).…”
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  13. 73
    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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  14. 74
    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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  15. 75
    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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  16. 76
    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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  17. 77
    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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  18. 78
    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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  19. 79
    Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multiple machine learning approaches.jpeg

    Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multipl... by Lili Huang (125493)

    Published 2025
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  20. 80
    Changes in GABA neurons (RiboTag::Gad2-Cre).

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

    Published 2022
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