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significant application » significant applications (Expand Search), significant implications (Expand Search), significant association (Expand Search)
application graphs » application rates (Expand Search), application rate (Expand Search), application range (Expand Search)
mean decrease » a decrease (Expand Search)
nn decrease » _ decrease (Expand Search), a decrease (Expand Search), gy decreased (Expand Search)
Showing 61 - 80 results of 13,386 for search '(( significant application graphs ) OR ( significant ((mean decrease) OR (nn decrease)) ))', query time: 0.43s Refine Results
  1. 61
    Study case in 4-regular graph.

    Study case in 4-regular graph. by Yeftanus Antonio (11608060)

    Published 2021
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  2. 62
    Heat map and k-means clustering of proteins (384) for which any Vpu-mutant was significantly different from wild type.

    Heat map and k-means clustering of proteins (384) for which any Vpu-mutant was significantly different from wild type. by Charlotte A. Stoneham (10819125)

    Published 2021
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  3. 63
    Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility

    Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility by Tianyuan Zheng (19265176)

    Published 2024
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  4. 64
    Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility

    Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility by Tianyuan Zheng (19265176)

    Published 2024
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  5. 65
    Summary of significant treatment × time effect.

    Summary of significant treatment × time effect. by Wensheng Xiao (16557866)

    Published 2024
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  6. 66
    Application of the short fixed intervals significantly improved primary data analysis.

    Application of the short fixed intervals significantly improved primary data analysis. by Alex A. Gutsol (6719063)

    Published 2019
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  7. 67
    A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening

    A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening by Fan Fan (341438)

    Published 2023
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  8. 68
    Four methods used for GraphMHC model validation: Intra-validation, inter-validation, clinical application, and extra-validation.

    Four methods used for GraphMHC model validation: Intra-validation, inter-validation, clinical application, and extra-validation. by Hoyeon Jeong (18264791)

    Published 2024
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  9. 69
    Mean case duration before and after intervention (t-statistic = -3.35, p <i>< </i>0.001), indicating a statistically significant difference in means.

    Mean case duration before and after intervention (t-statistic = -3.35, p <i>< </i>0.001), indicating a statistically significant difference in means. by Mohammad Bakhtiari (11838264)

    Published 2025
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  10. 70
    Decadal mean regional hydrology.

    Decadal mean regional hydrology. by Nic Gedney (19762301)

    Published 2024
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  11. 71
    Feasible solutions for the graph of Fig 1.

    Feasible solutions for the graph of Fig 1. by Md. Rafiqul Islam (403453)

    Published 2024
    “…Although it is an elementary graph partitioning problem, it is one of the most challenging combinatorial optimization-based problems, and tons of application areas make this problem highly admissible. …”
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  12. 72
    Line graph showing the increase or decrease in the average number of bee swarms caught over the past three years (Mean 

    Line graph showing the increase or decrease in the average number of bee swarms caught over the past three years (Mean  by Beatrice T. Nganso (4114768)

    Published 2025
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  13. 73
    CFU-Mk is significantly decreased by GATA1s overexpression in <i>GATA1s</i> strains.

    CFU-Mk is significantly decreased by GATA1s overexpression in <i>GATA1s</i> strains. by Shiori Matsuo (10492159)

    Published 2021
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  14. 74
    Most significant muscle activation values.

    Most significant muscle activation values. by María Benito-de-Pedro (22057468)

    Published 2025
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  15. 75
    Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points

    Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points by Haobo Wang (2529424)

    Published 2023
    “…By introducing molecular graph (MG) contrastive learning, MG-based ρ<sub>s</sub> and molecular inconsistency in activities (<i>I</i><sub>A</sub>) were calculated and employed for characterizing the structure–activity landscape (SAL)-based applicability domain AD<sub>SAL</sub>{ρ<sub>s</sub>, <i>I</i><sub>A</sub>}. …”
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  16. 76
    Summary of significant single-arm time effect.

    Summary of significant single-arm time effect. by Wensheng Xiao (16557866)

    Published 2024
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  17. 77
    Expression of <i>GmNN1/FT2a</i> in various tissues of soybean plants.

    Expression of <i>GmNN1/FT2a</i> in various tissues of soybean plants. by Xinxin Li (281237)

    Published 2022
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  18. 78
    GmNN1/FT2a interacted with GmNYFA-C in the BiFC assay.

    GmNN1/FT2a interacted with GmNYFA-C in the BiFC assay. by Xinxin Li (281237)

    Published 2022
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  19. 79
    Error boxplot and violin graphs for the previously published and proposed GPR models.

    Error boxplot and violin graphs for the previously published and proposed GPR models. by Fahd Saeed Alakbari (10701871)

    Published 2025
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  20. 80
    Trends in mean serum lipid profiles in patients with type 2 diabetes.

    Trends in mean serum lipid profiles in patients with type 2 diabetes. by Amirhossein Yadegar (13922190)

    Published 2023
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