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derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
values derived » virus derived (Expand Search), features derived (Expand Search)
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based codon » based color (Expand Search), based cohort (Expand Search), based action (Expand Search)
Showing 1 - 7 results of 7 for search '(( binary values derived optimization algorithm ) OR ( lines based codon optimization algorithm ))', query time: 0.93s Refine Results
  1. 1
    QA vs GA for 10 full-length proteins.

    QA vs GA for 10 full-length proteins. by Dillion M. Fox (4043159)

    Published 2021
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  2. 2
    Eigenvalues measured by QA vs GA scores.

    Eigenvalues measured by QA vs GA scores. by Dillion M. Fox (4043159)

    Published 2021
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  3. 3
    An example of a type of texture of binary preferred/non-preferred ranges of raw vegetation index values type feature.

    An example of a type of texture of binary preferred/non-preferred ranges of raw vegetation index values type feature. by Elise Varaela Voltura (17592996)

    Published 2023
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  4. 4
    An example of a type of percent cover of preferred ranges of vegetation index texture values type feature.

    An example of a type of percent cover of preferred ranges of vegetation index texture values type feature. by Elise Varaela Voltura (17592996)

    Published 2023
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  5. 5
    Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes

    Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes by Yu Y. (3096192)

    Published 2022
    “…We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. …”
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  6. 6
    An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%.

    An exemplar procedure for activity flow modeling based on the direct and shortest paths at a network density of 15%. by Zhengdong Wang (35299)

    Published 2025
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  7. 7
    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles

    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”
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