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Showing 1 - 20 results of 88 for search '(( gene based objective optimization algorithm ) OR ( binary image smart optimization algorithm ))', query time: 1.05s 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
    GeneScan profiles of human peripheral blood samples.

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

    Published 2022
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  10. 10
    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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  11. 11
    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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  12. 12
    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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  13. 13
    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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  14. 14
    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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  15. 15
    <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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  16. 16
    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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  17. 17
    Cauchy mutation.

    Cauchy mutation. by Dmitrii Smirnov (8822324)

    Published 2020
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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
    Sampled population parameters and corresponding node-based metrics, node removal statistics and BRIDES results.

    Sampled population parameters and corresponding node-based metrics, node removal statistics and BRIDES results. by Cindy Bouchard (6844979)

    Published 2022
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  20. 20
    Population graph of 19 wood turtle populations based on conditional genetic distance (cGD).

    Population graph of 19 wood turtle populations based on conditional genetic distance (cGD). by Cindy Bouchard (6844979)

    Published 2022
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