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initialization algorithm » optimization algorithms (Expand Search), maximization algorithm (Expand Search), identification algorithm (Expand Search)
process optimization » model optimization (Expand Search)
code initialization » node initialization (Expand Search), upon initialization (Expand Search), random initialization (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
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Showing 61 - 80 results of 194 for search '(( primary data code initialization algorithm ) OR ( genes based process optimization algorithm ))', query time: 0.73s Refine Results
  1. 61
    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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  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
    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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  5. 65
    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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  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
    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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  9. 69
    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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  10. 70
    Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy

    Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

    Published 2024
    “…Initially, separate algorithms based on sequence homology are utilized to assess this variant set. …”
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  11. 71
    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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  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
    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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  14. 74
    BOFdat Step 3: Identifying species-specific metabolic end goals.

    BOFdat Step 3: Identifying species-specific metabolic end goals. by Jean-Christophe Lachance (6619307)

    Published 2019
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  15. 75
    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. 76
    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. 77
    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. 78
    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. 79
    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. 80
    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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