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Showing 21 - 35 results of 35 for search '(( binary task driven optimization algorithm ) OR ( primary data robust estimation algorithm ))', query time: 0.28s Refine Results
  1. 21
    Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS

    Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS by Hui Tang (226667)

    Published 2019
    “…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”
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  2. 22
    Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX

    Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

    Published 2019
    “…Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”
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  3. 23
    DataSheet1_Development and validation of a prognostic prediction model for iron metabolism-related genes in patients with pancreatic adenocarcinoma.ZIP

    DataSheet1_Development and validation of a prognostic prediction model for iron metabolism-related genes in patients with pancreatic adenocarcinoma.ZIP by Wenhan Wei (14325063)

    Published 2023
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  4. 24
    Data Sheet 1_Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.docx

    Data Sheet 1_Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor.docx by Jeremy A. Balch (13228956)

    Published 2024
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  5. 25
    Data Sheet 1_Assessment of stress hyperglycemia ratio to predict mortality in critically ill patients with sepsis: a retrospective cohort study from the MIMIC-IV database.docx

    Data Sheet 1_Assessment of stress hyperglycemia ratio to predict mortality in critically ill patients with sepsis: a retrospective cohort study from the MIMIC-IV database.docx by Dong Xia (54339)

    Published 2025
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  6. 26
    Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4

    Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4 by Matthew Yedutenko (5142461)

    Published 2025
    “…<p>Motion detection is a primary task required for robotic systems to perceive and navigate in their environment. …”
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  7. 27
    Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Background<p>Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. …”
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  8. 28
    Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Background<p>Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. …”
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  9. 29
    Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx

    Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

    Published 2025
    “…Background<p>Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. …”
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  10. 30
    Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Background<p>Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. …”
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  11. 31
    Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Background<p>Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. …”
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  12. 32
    Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx

    Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

    Published 2025
    “…Background<p>Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. …”
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  13. 33
    Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif

    Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

    Published 2025
    “…Background<p>Glioblastoma (GBM) is the most aggressive primary malignancy of the central nervous system, characterized by profound heterogeneity and an immunosuppressive microenvironment, leading to dismal prognosis. …”
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  14. 34
    Reimagining the Kendall plot: using <i>δ</i><sup>15</sup>N and <i>δ</i><sup>18</sup>O of nitrate and advanced machine learning to improve N pollutant source classification

    Reimagining the Kendall plot: using <i>δ</i><sup>15</sup>N and <i>δ</i><sup>18</sup>O of nitrate and advanced machine learning to improve N pollutant source classification by Katarzyna Samborska-Goik (20926968)

    Published 2025
    “…The stable isotope ratios of nitrate (<i>δ</i><sup>15</sup>N, <i>δ</i><sup>18</sup>O) are widely used as tracers of nitrogen pollution sources. The primary technique for identifying nitrate sources has been the longstanding Kendall boxplot, despite improved methods using Bayes’ theorem and the R language for estimating source fractions using hydrogeochemical context, N source data and expert assessment. …”
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  15. 35
    Supplementary file 1_Healthcare deprivation matters: a novel framework to unveil the influencing mechanisms of aging anxiety and healthcare utilization.docx

    Supplementary file 1_Healthcare deprivation matters: a novel framework to unveil the influencing mechanisms of aging anxiety and healthcare utilization.docx by Zhiyi Luo (22424863)

    Published 2025
    “…The random forest algorithm is used to estimate the marginal effect of aging anxiety on healthcare utilization. …”
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