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Showing 1 - 20 results of 140 for search '(( binary basic based optimization algorithm ) OR ( lines b cell optimization algorithm ))', query time: 0.67s Refine Results
  1. 1
    Line plots of ||.

    Line plots of ||. by Maryam Arianpouya (12299222)

    Published 2024
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  2. 2
    Visual Clustering Results of Algorithms on Four Lines Dataset.

    Visual Clustering Results of Algorithms on Four Lines Dataset. by Wei Xingqiong (21492703)

    Published 2025
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  3. 3
    Details of 23 basic benchmark functions.

    Details of 23 basic benchmark functions. by Ying Li (38224)

    Published 2024
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  4. 4
    Graphic representation of the CAP A events identified by the algorithm (blue line) and by the scorer (red line).

    Graphic representation of the CAP A events identified by the algorithm (blue line) and by the scorer (red line). by Maria Paola Tramonti Fantozzi (11785619)

    Published 2021
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  5. 5
    The dynamic stress of the mechanism varies with width b of A<sub>1</sub>B<sub>1</sub> section.

    The dynamic stress of the mechanism varies with width b of A<sub>1</sub>B<sub>1</sub> section. by SHANG Xin (14559176)

    Published 2023
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  6. 6
    The dynamic stress of the mechanism varies with width b of the A<sub>1</sub>B<sub>1</sub> section.

    The dynamic stress of the mechanism varies with width b of the A<sub>1</sub>B<sub>1</sub> section. by SHANG Xin (14559176)

    Published 2023
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  7. 7
    The distribution of SVs in truth sets and the performance of individual algorithms.

    The distribution of SVs in truth sets and the performance of individual algorithms. by De-Min Duan (20685513)

    Published 2025
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  8. 8
    Models generated by the optimized calibration pipeline predict cell-specific arrhythmia susceptibility.

    Models generated by the optimized calibration pipeline predict cell-specific arrhythmia susceptibility. by Janice Yang (5747246)

    Published 2024
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  9. 9
    The dynamic stress of the mechanism varies with diameter d of B<sub>1</sub>C<sub>1</sub>.

    The dynamic stress of the mechanism varies with diameter d of B<sub>1</sub>C<sub>1</sub>. by SHANG Xin (14559176)

    Published 2023
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  10. 10
    Generation of in silico dataset and calibration pipeline optimization.

    Generation of in silico dataset and calibration pipeline optimization. by Janice Yang (5747246)

    Published 2024
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  11. 11
    Rationale for the calculation of the optimal .

    Rationale for the calculation of the optimal . by Irina Veith (10499821)

    Published 2021
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  12. 12
    Highly sensitive conductance parameters are well-constrained by the optimized calibration pipeline.

    Highly sensitive conductance parameters are well-constrained by the optimized calibration pipeline. by Janice Yang (5747246)

    Published 2024
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  13. 13
    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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  14. 14
    Optimal <i>Q</i> analysis for chromosome 10.

    Optimal <i>Q</i> analysis for chromosome 10. by Dolores Bernenko (16528426)

    Published 2023
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  15. 15
    Distribution and survival based cutoff optimization for proportion of D2-40 stained sinus with respect to overall survival by “Cutoff Finder” algorithm.

    Distribution and survival based cutoff optimization for proportion of D2-40 stained sinus with respect to overall survival by “Cutoff Finder” algorithm. by Christina C. Westhoff (14498351)

    Published 2023
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  16. 16
    |B1+| and |E| maps using the standard basis.

    |B1+| and |E| maps using the standard basis. by Maryam Arianpouya (12299222)

    Published 2024
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  17. 17
    Cell-free DNA coverage and fragmentomic profiles produced under various sequencing protocols

    Cell-free DNA coverage and fragmentomic profiles produced under various sequencing protocols by Antoine Passemiers (17302912)

    Published 2024
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  18. 18
    Optimization of <i>KIR3DL1/S1</i> allele imputation using data from Europeans.

    Optimization of <i>KIR3DL1/S1</i> allele imputation using data from Europeans. by Genelle F. Harrison (12119661)

    Published 2022
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  19. 19
    Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives

    Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives by Carlos Santiago (13227143)

    Published 2022
    “…A computational high-throughput screening (cHTS) study of 2-acylpyrroles <b>5a</b>,<b>b</b> has been performed calculating >20,700 activity scores <i>vs</i> a large space of 647 assays involving multiple <i>Leishmania</i> species, cell lines, and potential target proteins. …”
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  20. 20
    Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives

    Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives by Carlos Santiago (13227143)

    Published 2022
    “…A computational high-throughput screening (cHTS) study of 2-acylpyrroles <b>5a</b>,<b>b</b> has been performed calculating >20,700 activity scores <i>vs</i> a large space of 647 assays involving multiple <i>Leishmania</i> species, cell lines, and potential target proteins. …”
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