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Showing 1 - 20 results of 28 for search '(( degs based testing optimization algorithm ) OR ( binary image driven optimization algorithm ))', query time: 0.65s Refine Results
  1. 1
    Experimental observation of gastruloids and shape quantification using LOCO-EFA.

    Experimental observation of gastruloids and shape quantification using LOCO-EFA. by Martijn A. de Jong (17896732)

    Published 2024
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  2. 2
    Table1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.xlsx

    Table1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.xlsx by Krish Mendapara (18890758)

    Published 2024
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  3. 3
    Image2_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png

    Image2_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png by Krish Mendapara (18890758)

    Published 2024
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  4. 4
    Image3_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png

    Image3_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png by Krish Mendapara (18890758)

    Published 2024
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  5. 5
    Image1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png

    Image1_Development and evaluation of a chronic kidney disease risk prediction model using random forest.png by Krish Mendapara (18890758)

    Published 2024
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  6. 6
    Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png

    Image 1_A multimodal AI-driven framework for cardiovascular screening and risk assessment in diverse athletic populations: innovations in sports cardiology.png by Minjin Guo (22751300)

    Published 2025
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  7. 7
    Table1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.xlsx

    Table1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.xlsx by Yu Yin (329063)

    Published 2023
    “…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”
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  8. 8
    Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf

    Image1_Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.pdf by Yu Yin (329063)

    Published 2023
    “…In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes.…”
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  9. 9
    Thesis-RAMIS-Figs_Slides

    Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

    Published 2024
    “…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”
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  10. 10
    Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx

    Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx by Haolong Zhang (13911013)

    Published 2023
    “…The optimal model was determined based on the AUC values derived from various algorithms. …”
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  11. 11
    Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx

    Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx by Zhang Haolong (17456619)

    Published 2023
    “…The optimal model was determined based on the AUC values derived from various algorithms. …”
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  12. 12
    DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx

    DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx by Haolong Zhang (13911013)

    Published 2023
    “…The optimal model was determined based on the AUC values derived from various algorithms. …”
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  13. 13
    DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx

    DataSheet1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx by Zhang Haolong (17456619)

    Published 2023
    “…The optimal model was determined based on the AUC values derived from various algorithms. …”
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  14. 14
    Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multiple machine learning approaches.jpeg

    Image 1_Construction of a diagnostic model and identification of effect genes for diabetic kidney disease with concurrent vascular calcification based on bioinformatics and multipl... by Lili Huang (125493)

    Published 2025
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  15. 15
    Image 4_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 4_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  16. 16
    Image 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif

    Image 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  17. 17
    Image 7_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif

    Image 7_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.tif by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  18. 18
    Image 2_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 2_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  19. 19
    Image 3_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf

    Image 3_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.pdf by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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  20. 20
    Data Sheet 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.zip

    Data Sheet 1_Integrative single-cell and exosomal multi-omics uncovers SCNN1A and EFNA1 as non-invasive biomarkers and drivers of ovarian cancer metastasis.zip by Liping Tang (77094)

    Published 2025
    “…We then applied ten machine learning algorithm to exosomal transcriptomic data to evaluate diagnostic performance and identify the optimal classifier. …”
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