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objects optimization » objectives optimization (Expand Search), objective optimization (Expand Search), robust optimization (Expand Search)
cell optimization » field optimization (Expand Search), wolf optimization (Expand Search), lead optimization (Expand Search)
based objects » based object (Expand Search)
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Showing 1 - 20 results of 98 for search '(( gene based objects optimization algorithm ) OR ( binary mask cell optimization algorithm ))', query time: 0.66s Refine Results
  1. 1
    Optimal image settings for object detection of Iba1 immunolabelled cells in DAB.

    Optimal image settings for object detection of Iba1 immunolabelled cells in DAB. by Danish M. Anwer (15383441)

    Published 2023
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  2. 2
    Data_Sheet_3_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_3_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  3. 3
    Data_Sheet_7_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_7_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  4. 4
    Data_Sheet_2_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.ZIP

    Data_Sheet_2_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.ZIP by Vishnu Ramasubramanian (4951354)

    Published 2021
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  5. 5
    Data_Sheet_4_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx

    Data_Sheet_4_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  6. 6
    Data_Sheet_6_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx

    Data_Sheet_6_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.docx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  7. 7
    Data_Sheet_1_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.PDF

    Data_Sheet_1_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.PDF by Vishnu Ramasubramanian (4951354)

    Published 2021
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  8. 8
    Data_Sheet_5_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx

    Data_Sheet_5_Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean.xlsx by Vishnu Ramasubramanian (4951354)

    Published 2021
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  9. 9
    A* Path-Finding Algorithm to Determine Cell Connections

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

    Published 2025
    “…The A* algorithm then evaluated connectivity by minimizing Euclidean distance and heuristic cost between cells. …”
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  10. 10
    GeneScan profiles of human peripheral blood samples.

    GeneScan profiles of human peripheral blood samples. by Nika Abdollahi (4966120)

    Published 2022
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  11. 11
    Illustration of the BRIDES algorithm and the selection process.

    Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

    Published 2022
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  12. 12
    IGHV/J gene usage distribution of the AMR1 repertoire compared to the polyclonal background.

    IGHV/J gene usage distribution of the AMR1 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

    Published 2022
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  13. 13
    IGHV/J gene usage distribution of the AMR2 repertoire compared to the polyclonal background.

    IGHV/J gene usage distribution of the AMR2 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

    Published 2022
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  14. 14
    IGHV/J gene usage distribution of the AMR3 repertoire compared to the polyclonal background.

    IGHV/J gene usage distribution of the AMR3 repertoire compared to the polyclonal background. by Nika Abdollahi (4966120)

    Published 2022
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  15. 15
    Overview of the segmentation algorithm.

    Overview of the segmentation algorithm. by Michael C. Robitaille (11949174)

    Published 2022
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  16. 16
    Distributions of model parameters mirror differentially expressed ion channel genes.

    Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

    Published 2022
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  17. 17
    <i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling

    <i>OptRAM</i>: <i>In-silico</i> strain design via integrative regulatory-metabolic network modeling by Fangzhou Shen (5140994)

    Published 2019
    “…To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. …”
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  18. 18
    Random mutation direction in multi-dimensional parameter space.

    Random mutation direction in multi-dimensional parameter space. by Dmitrii Smirnov (8822324)

    Published 2020
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  19. 19
    Cauchy mutation.

    Cauchy mutation. by Dmitrii Smirnov (8822324)

    Published 2020
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  20. 20
    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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