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Showing 1 - 20 results of 29 for search '(( genes based property optimization algorithm ) OR ( binary image driven optimization algorithm ))', query time: 0.71s Refine Results
  1. 1
    Algorithm verification.

    Algorithm verification. by Dmitrii Smirnov (8822324)

    Published 2020
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  2. 2
    Algorithm 2: Maintain inhibitory densities coherence.

    Algorithm 2: Maintain inhibitory densities coherence. by Dimitri Rodarie (10148838)

    Published 2022
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  3. 3
    Algorithm 1: Cap inhibitory densities to number of neurons.

    Algorithm 1: Cap inhibitory densities to number of neurons. by Dimitri Rodarie (10148838)

    Published 2022
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  4. 4
    Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

    Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

    Published 2023
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  5. 5
    Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

    Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

    Published 2023
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  6. 6
    Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

    Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures by Dibyajyoti Das (14845321)

    Published 2023
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  7. 7
    DataSheet_1_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.docx

    DataSheet_1_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.docx by Daniela Tizabi (10297654)

    Published 2022
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  8. 8
    DataSheet_2_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.pdf

    DataSheet_2_Comparative analysis of assembly algorithms to optimize biosynthetic gene cluster identification in novel marine actinomycete genomes.pdf by Daniela Tizabi (10297654)

    Published 2022
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  9. 9
    Illustration of crossover.

    Illustration of crossover. by Ruochen Zhang (3434996)

    Published 2024
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  10. 10
    Image_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF

    Image_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF... by Huangwei Wei (17011266)

    Published 2023
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  11. 11
    Table_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.XLSX

    Table_1_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.XLS... by Huangwei Wei (17011266)

    Published 2023
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  12. 12
    Image_2_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF

    Image_2_Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease.TIF... by Huangwei Wei (17011266)

    Published 2023
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  13. 13
    Experimental observation of gastruloids and shape quantification using LOCO-EFA.

    Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

    Published 2024
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  14. 14
    Presentation_1_Calibrating Seed-Based Heuristics to Map Short Reads With Sesame.zip

    Presentation_1_Calibrating Seed-Based Heuristics to Map Short Reads With Sesame.zip by Guillaume J. Filion (9027317)

    Published 2020
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  15. 15
    The brief description on the WTCCC dataset.

    The brief description on the WTCCC dataset. by Liyan Sun (760586)

    Published 2024
    “…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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  16. 16
    The penetrance tables for the 8 DNME models.

    The penetrance tables for the 8 DNME models. by Liyan Sun (760586)

    Published 2024
    “…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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  17. 17
    The penetrance tables for the 8 DME models.

    The penetrance tables for the 8 DME models. by Liyan Sun (760586)

    Published 2024
    “…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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  18. 18
    The penetrance tables for the 6 DNME3 models.

    The penetrance tables for the 6 DNME3 models. by Liyan Sun (760586)

    Published 2024
    “…Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions.…”
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  19. 19
    Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png

    Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

    Published 2025
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  20. 20
    Table1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.docx

    Table1_Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO.docx by Haohan Xue (17892128)

    Published 2024
    “…The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. …”
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