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Showing 1 - 20 results of 37 for search 'multiple l detection algorithm', query time: 0.31s Refine Results
  1. 1
    Algorithm detection steps.

    Algorithm detection steps. by José M. Rivera-Arbeláez (12418512)

    Published 2025
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  2. 2
    Multiple sequence alignment of the <i>Strumpellin</i> coding sequences performed by Geneious v.3.6.2 program.

    Multiple sequence alignment of the <i>Strumpellin</i> coding sequences performed by Geneious v.3.6.2 program. by Yusr Saadi-Ben Aoun (20469385)

    Published 2024
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  3. 3
    Action potential detection with high temporal accuracy using a convolutional neural network (CNN).

    Action potential detection with high temporal accuracy using a convolutional neural network (CNN). by Tjitse van der Molen (20391106)

    Published 2024
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  4. 4
    Action potential detection with high temporal accuracy using a convolutional neural network (CNN).

    Action potential detection with high temporal accuracy using a convolutional neural network (CNN). by Tjitse van der Molen (20391106)

    Published 2024
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  5. 5
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  6. 6
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  7. 7
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  8. 8
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  9. 9
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  10. 10
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  11. 11
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  12. 12
    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

    A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins by Jonathan Chiu-Chun Chou (22184735)

    Published 2025
    “…Here, we present <u>S</u>tructure-based <u>L</u>ipid-<u>i</u>nteracting <u>P</u>ocket <u>P</u>redictor (SLiPP), an algorithm that leverages machine learning to detect protein cavities capable of binding to lipids in protein structures. …”
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  13. 13
    Users data points.

    Users data points. by Alon Sela (12231559)

    Published 2025
    “…While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. …”
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  14. 14
    Bipartite network data points.

    Bipartite network data points. by Alon Sela (12231559)

    Published 2025
    “…While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. …”
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  15. 15
    Illustration of word-to-tweet bipartite graph.

    Illustration of word-to-tweet bipartite graph. by Alon Sela (12231559)

    Published 2025
    “…While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. …”
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  16. 16
    Hashtags data points.

    Hashtags data points. by Alon Sela (12231559)

    Published 2025
    “…While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. …”
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  17. 17
    Data sets used for user analysis.

    Data sets used for user analysis. by Alon Sela (12231559)

    Published 2025
    “…While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. …”
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  18. 18
    Data sets used for hashtag analysis.

    Data sets used for hashtag analysis. by Alon Sela (12231559)

    Published 2025
    “…While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. …”
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  19. 19
    S1 Graphical abstract -

    S1 Graphical abstract - by José M. Rivera-Arbeláez (12418512)

    Published 2025
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  20. 20
    Data processing flow diagram.

    Data processing flow diagram. by Carl D. Snyder (20868650)

    Published 2025
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