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Showing 41 - 60 results of 4,678 for search '(( algorithm python function ) OR ((( algorithm low functional ) OR ( algorithm l function ))))', query time: 0.74s Refine Results
  1. 41
    Coding regions of the HIV-1 genome.

    Coding regions of the HIV-1 genome. by Jordan P. Skittrall (5100746)

    Published 2019
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  2. 42
    Cumulative sums of nMPDs.

    Cumulative sums of nMPDs. by Jordan P. Skittrall (5100746)

    Published 2019
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  3. 43
    RNAalifold output for local folding of alignment region of interest corresponding to HXB2 nucleotide reference 4749–5093.

    RNAalifold output for local folding of alignment region of interest corresponding to HXB2 nucleotide reference 4749–5093. by Jordan P. Skittrall (5100746)

    Published 2019
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  4. 44
    Relative frequencies of nucleotides in the Kozak sequences of the 561 whole genome sequences in our analysis at putative open reading frames.

    Relative frequencies of nucleotides in the Kozak sequences of the 561 whole genome sequences in our analysis at putative open reading frames. by Jordan P. Skittrall (5100746)

    Published 2019
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  5. 45
    Schematic representations of coding potential of the 561 whole genome sequences in our analysis at putative open reading frames.

    Schematic representations of coding potential of the 561 whole genome sequences in our analysis at putative open reading frames. by Jordan P. Skittrall (5100746)

    Published 2019
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  6. 46
    RNAalifold output for local folding of alignment region of interest corresponding to HXB2 reference 7662–8051.

    RNAalifold output for local folding of alignment region of interest corresponding to HXB2 reference 7662–8051. by Jordan P. Skittrall (5100746)

    Published 2019
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  7. 47
    Detail of splice-dependent bridging of upstream elements by the region HXB2 6206–6226.

    Detail of splice-dependent bridging of upstream elements by the region HXB2 6206–6226. by Jordan P. Skittrall (5100746)

    Published 2019
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  8. 48
    Cumulative sums of nMPDs.

    Cumulative sums of nMPDs. by Jordan P. Skittrall (5100746)

    Published 2019
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  9. 49
    ROMK mutations from TOPMed and ClinVar show varying growth defects in yeast in low potassium medium.

    ROMK mutations from TOPMed and ClinVar show varying growth defects in yeast in low potassium medium. by Nga H. Nguyen (2075920)

    Published 2023
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  10. 50
    Model1.3D exhibits a low MSE.

    Model1.3D exhibits a low MSE. by Robin Dirk (17296179)

    Published 2023
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  11. 51
    Algorithm membership function.

    Algorithm membership function. by Mohamed Raef Smaoui (9865830)

    Published 2022
    “…<p>(Top) Input Membership Function. The algorithm classifies glucose input into 4 sets: low, medium, high, and ex_high. …”
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  12. 52
    Box plot of the algorithms based on 12 benchmark functions.

    Box plot of the algorithms based on 12 benchmark functions. by Chuang Zhang (2199421)

    Published 2023
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  13. 53
    Convergence behavior of the algorithms based on 12 benchmark functions.

    Convergence behavior of the algorithms based on 12 benchmark functions. by Chuang Zhang (2199421)

    Published 2023
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  14. 54
    Comparison of details of different algorithms.

    Comparison of details of different algorithms. by Hao Wu (65943)

    Published 2024
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  15. 55
    Structure of the LMPEC algorithm model.

    Structure of the LMPEC algorithm model. by Hao Wu (65943)

    Published 2024
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  16. 56
    Python scripts used to model CPU errors by Poisson distribution density mass function and to draw graph of Poisson events (needs Matplot, SciPy, PysimpleGUI and NumPy)

    Python scripts used to model CPU errors by Poisson distribution density mass function and to draw graph of Poisson events (needs Matplot, SciPy, PysimpleGUI and NumPy) by Santhosh Kumar Rajamani (13023678)

    Published 2022
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  17. 57
    IGD evolution trends of four algorithms on test functions.

    IGD evolution trends of four algorithms on test functions. by Jun Sun (48981)

    Published 2023
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  18. 58
    Contrast enhancement of digital images using dragonfly algorithm

    Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

    Published 2024
    “…The experimental observations reveal that the proposed DA-based image contrast enhancement produces high-quality images from its low-contrast counterparts. Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. …”
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  19. 59
    Unimodal benchmark functions.

    Unimodal benchmark functions. by Yu-Jun Zhang (185411)

    Published 2022
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  20. 60
    <i>in vitro</i> organoids are predicted to have rather low dispersion parameter.

    <i>in vitro</i> organoids are predicted to have rather low dispersion parameter. by Robin Dirk (17296179)

    Published 2023
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