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Showing 141 - 160 results of 2,525 for search '(((( algorithm pre functional ) OR ( algorithm could function ))) OR ( algorithm python function ))', query time: 0.54s Refine Results
  1. 141
    Features selected by optimization algorithms.

    Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

    Published 2024
    “…Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. The suggested strategy facilitates the acquisition of precise functionality attributes, hence simplifying the detection procedure. …”
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  2. 142
    A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction methods.

    A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

    Published 2025
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  3. 143
    Principal component analysis (PCA) of sub-layer mean intensities.

    Principal component analysis (PCA) of sub-layer mean intensities. by Maëlle Vilbert (17101954)

    Published 2023
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  4. 144
    Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart.

    Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

    Published 2025
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  5. 145
    An miRNA panel to predict RRiF.

    An miRNA panel to predict RRiF. by Nabarun Chakraborty (535772)

    Published 2024
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  6. 146
    Cost functions implemented in Neuroptimus.

    Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

    Published 2024
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  7. 147
    Parametric study of accuracy of the Random forest algorithm for different proportions of test samples and different choice of random states.

    Parametric study of accuracy of the Random forest algorithm for different proportions of test samples and different choice of random states. by Abeer Aljohani (18497914)

    Published 2025
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  8. 148
    Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval.

    Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval. by Achim Langenbucher (3156741)

    Published 2022
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  9. 149
    Optimised formula constants for the SRKT, the Hoffer Q, the Holladay 1, Haigis (with optimised a0 and preset values a1 = 0.4 / a2 = 0.1, Haigis1; and with optimised a0 / a1 / a2 constant triplet, Haigis3), and Castrop formula.

    Optimised formula constants for the SRKT, the Hoffer Q, the Holladay 1, Haigis (with optimised a0 and preset values a1 = 0.4 / a2 = 0.1, Haigis1; and with optimised a0 / a1 / a2 co... by Achim Langenbucher (3156741)

    Published 2022
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  10. 150
    Cumulative density function (CDF) of prediction error (PE, achieved spherical equivalent–formula predicted spherical equivalent) for the SRKT, Hoffer Q, Holladay1, Haigis (with optimised a0 and preset values a1 = 0.4 / a2 = 0.1 (Haigis1); and with optimised a0 / a1 / a2 constant triplet (Haigis3)), and Castrop formula.

    Cumulative density function (CDF) of prediction error (PE, achieved spherical equivalent–formula predicted spherical equivalent) for the SRKT, Hoffer Q, Holladay1, Haigis (with opt... by Achim Langenbucher (3156741)

    Published 2022
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  11. 151
    Prediction error (PE) as the difference between achieved and formula predicted spherical equivalent for 8 different statistical metrics of formula constant optimisation and various formulae under test.

    Prediction error (PE) as the difference between achieved and formula predicted spherical equivalent for 8 different statistical metrics of formula constant optimisation and various... by Achim Langenbucher (3156741)

    Published 2022
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  12. 152
    Parameters.

    Parameters. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  13. 153
    Parameters.

    Parameters. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  14. 154
    <sup>13</sup>C-MFA model.

    <sup>13</sup>C-MFA model. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  15. 155
    Parameters.

    Parameters. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  16. 156
    Supplementary information 2.

    Supplementary information 2. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  17. 157
    Parameters.

    Parameters. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  18. 158
    Parameters.

    Parameters. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  19. 159
    Supplementary information 3.

    Supplementary information 3. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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  20. 160
    Variables.

    Variables. by Elias Vera-Siguenza (16993830)

    Published 2023
    “…This property may be a telling sign that pre-clinical tumour metabolism studies exclusively utilising <i>in-vitro</i> mono-culture models could prove to be limited for uncovering novel metabolic targets able to translate into clinical therapies. …”
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