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Showing 1 - 4 results of 4 for search '(((("data modeling algorithm") OR ("modeled using algorithm"))) OR ("element method algorithm"))', query time: 0.26s Refine Results
  1. 1
    Data Sheet 1_Predicting reverse-bound peptide conformations in MHC Class II with PANDORA.pdf

    Data Sheet 1_Predicting reverse-bound peptide conformations in MHC Class II with PANDORA.pdf by Daniel T. Rademaker (12521431)

    Published 2025
    “…PANDORA has overcome this challenge through integrative modeling using algorithmically reversed peptides as templates. …”
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  2. 2
    Presentation 1_Research on the optimization model of anti-breast cancer candidate drugs based on machine learning.zip

    Presentation 1_Research on the optimization model of anti-breast cancer candidate drugs based on machine learning.zip by Zhou Dong (6293171)

    Published 2025
    “…The constructed Quantitative Structure-Activity Relationship (QSAR) model, using algorithms such as LightGBM, Random Forest, and XGBoost, achieved an R<sup>2</sup> value of 0.743 for biological activity prediction, demonstrating strong predictive performance. …”
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  3. 3
    Data Sheet 1_Research on the optimization model of anti-breast cancer candidate drugs based on machine learning.zip

    Data Sheet 1_Research on the optimization model of anti-breast cancer candidate drugs based on machine learning.zip by Zhou Dong (6293171)

    Published 2025
    “…The constructed Quantitative Structure-Activity Relationship (QSAR) model, using algorithms such as LightGBM, Random Forest, and XGBoost, achieved an R<sup>2</sup> value of 0.743 for biological activity prediction, demonstrating strong predictive performance. …”
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  4. 4
    Data Sheet 2_Research on the optimization model of anti-breast cancer candidate drugs based on machine learning.zip

    Data Sheet 2_Research on the optimization model of anti-breast cancer candidate drugs based on machine learning.zip by Zhou Dong (6293171)

    Published 2025
    “…The constructed Quantitative Structure-Activity Relationship (QSAR) model, using algorithms such as LightGBM, Random Forest, and XGBoost, achieved an R<sup>2</sup> value of 0.743 for biological activity prediction, demonstrating strong predictive performance. …”
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