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Showing 1 - 20 results of 52 for search '(( genes based fusion optimization algorithm ) OR ( binary based cell optimization algorithm ))', query time: 0.73s Refine Results
  1. 1
    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.XLSX

    Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

    Published 2022
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  2. 2
    Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses

    Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

    Published 2025
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  3. 3
    A* Path-Finding Algorithm to Determine Cell Connections

    A* Path-Finding Algorithm to Determine Cell Connections by Max Weng (22327159)

    Published 2025
    “…Pixel paths were classified using a z-score brightness threshold of 1.21, optimized for noise reduction and accuracy. The A* algorithm then evaluated connectivity by minimizing Euclidean distance and heuristic cost between cells. …”
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  4. 4
    Table1_Computational prediction of allergenic proteins based on multi-feature fusion.DOCX

    Table1_Computational prediction of allergenic proteins based on multi-feature fusion.DOCX by Bin Liu (5899)

    Published 2023
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  5. 5
    The illustration depicts our CQ-CNN architecture for binary image classification.

    The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

    Published 2025
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  6. 6
    ROC curve for binary classification.

    ROC curve for binary classification. by Nicodemus Songose Awarayi (18414494)

    Published 2024
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  7. 7
    Confusion matrix for binary classification.

    Confusion matrix for binary classification. by Nicodemus Songose Awarayi (18414494)

    Published 2024
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  8. 8
    Number of highest variance genes selected per omic for each dataset-approach combination.

    Number of highest variance genes selected per omic for each dataset-approach combination. by Teemu J. Rintala (19333003)

    Published 2024
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  9. 9
    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF

    Data_Sheet_1_A Global Optimizer for Nanoclusters.PDF by Maya Khatun (7437011)

    Published 2019
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  10. 10
    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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  11. 11
    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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  12. 12
    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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  13. 13
    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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  14. 14
    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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  15. 15
    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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  16. 16
    Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis.

    Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis. by Richard Porwancher (11413931)

    Published 2021
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  17. 17
    The architecture of LSTM cell.

    The architecture of LSTM cell. by Asmaa Ahmed Awad (16726315)

    Published 2023
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  18. 18
    Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF

    Data_Sheet_1_Crystal Structure Prediction of Binary Alloys via Deep Potential.PDF by Haidi Wang (9694004)

    Published 2020
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  19. 19
    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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  20. 20
    Stability of cancer clustering results with different pathway sets.

    Stability of cancer clustering results with different pathway sets. by Teemu J. Rintala (19333003)

    Published 2024
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