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Showing 161 - 180 results of 1,319 for search '(((( algorithm maml function ) OR ( algorithm step function ))) OR ( algorithm python function ))*', query time: 0.50s Refine Results
  1. 161
    Table 6_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 6_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
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  2. 162
    Table 13_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 13_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
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  3. 163
    Table 9_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 9_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
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  4. 164
    Table 2_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 2_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
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  5. 165
    Table 14_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 14_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
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  6. 166
    Table 5_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 5_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
    Save to List
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  7. 167
    Table 11_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 11_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
    Save to List
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  8. 168
    Table 1_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 1_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
    Save to List
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  9. 169
    Table 4_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 4_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
    Save to List
    Saved in:
  10. 170
    Table 7_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 7_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
    Save to List
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  11. 171
    Table 3_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx

    Table 3_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

    Published 2025
    “…These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.…”
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  12. 172
    wIRBMO feature selection algorithm improving rate.

    wIRBMO feature selection algorithm improving rate. by Chenyi Zhu (9383370)

    Published 2025
    Subjects:
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  13. 173
    Sensitivity values of IRBMO vs. other binary algorithms.

    Sensitivity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  14. 174
    Specificity values of IRBMO vs. other binary algorithms.

    Specificity values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  15. 175
    Fitness values of IRBMO vs. other binary algorithms.

    Fitness values of IRBMO vs. other binary algorithms. by Chenyi Zhu (9383370)

    Published 2025
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  16. 176
    Parselmouth for bioacoustics: automated acoustic analysis in Python

    Parselmouth for bioacoustics: automated acoustic analysis in Python by Yannick Jadoul (11498813)

    Published 2023
    “…Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. …”
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  17. 177
    We recorded the distribution function of the one-step prediction time for CLPREM and its comparative models, as well as a curve graph showing the multiple of the comparative model’s prediction time relative to CLPREM.

    We recorded the distribution function of the one-step prediction time for CLPREM and its comparative models, as well as a curve graph showing the multiple of the comparative model’... by Xiaorui Wu (8322705)

    Published 2024
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  18. 178
    Functional pattern of the proposed method for depression detection.

    Functional pattern of the proposed method for depression detection. by Umme Marzia Haque (11846815)

    Published 2021
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  19. 179
    Comparison of the fits of the original and mocked probability density functions to the long-tailed loglogistic, lognormal and generalized extreme value distributions.

    Comparison of the fits of the original and mocked probability density functions to the long-tailed loglogistic, lognormal and generalized extreme value distributions. by Michael Smith (26855)

    Published 2019
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  20. 180
    libEnsemble: New capabilities for coordinating dynamic ensembles of calculations

    libEnsemble: New capabilities for coordinating dynamic ensembles of calculations by John-Luke Navarro (9024008)

    Published 2023
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