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Showing 3,321 - 3,340 results of 9,924 for search '(((( algorithm python function ) OR ( algorithm both function ))) OR ( algorithm from functional ))', query time: 0.51s Refine Results
  1. 3321
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
    “…A conceptually opposite approach is provided by reference-free methods, opening to the on-the-fly generation of force maps from an ongoing experiment. This requires an image processing algorithm keeping the pace of the biological phenomena under investigation. …”
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  2. 3322
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
    “…A conceptually opposite approach is provided by reference-free methods, opening to the on-the-fly generation of force maps from an ongoing experiment. This requires an image processing algorithm keeping the pace of the biological phenomena under investigation. …”
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  3. 3323
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
    “…A conceptually opposite approach is provided by reference-free methods, opening to the on-the-fly generation of force maps from an ongoing experiment. This requires an image processing algorithm keeping the pace of the biological phenomena under investigation. …”
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  4. 3324
    Cellogram: On-the-Fly Traction Force Microscopy

    Cellogram: On-the-Fly Traction Force Microscopy by Tobias Lendenmann (2623534)

    Published 2019
    “…A conceptually opposite approach is provided by reference-free methods, opening to the on-the-fly generation of force maps from an ongoing experiment. This requires an image processing algorithm keeping the pace of the biological phenomena under investigation. …”
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  5. 3325
    Image2_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF

    Image2_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF by Yan Feng (128912)

    Published 2022
    “…After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. …”
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  6. 3326
    Table1_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.DOCX

    Table1_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.DOCX by Yan Feng (128912)

    Published 2022
    “…After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. …”
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  7. 3327
    Image4_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF

    Image4_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF by Yan Feng (128912)

    Published 2022
    “…After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. …”
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  8. 3328
    Image1_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF

    Image1_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF by Yan Feng (128912)

    Published 2022
    “…After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. …”
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  9. 3329
    Image3_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF

    Image3_Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.TIF by Yan Feng (128912)

    Published 2022
    “…After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. …”
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  10. 3330
    Adaptive Memory and <i>In Materia</i> Reinforcement Learning Enabled by Flexoelectric-like Response from Ultrathin HfO<sub>2</sub>

    Adaptive Memory and <i>In Materia</i> Reinforcement Learning Enabled by Flexoelectric-like Response from Ultrathin HfO<sub>2</sub> by Mohit Kumar (399134)

    Published 2022
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  11. 3331
    Constructing Accurate Potential Energy Surfaces with Limited High-Level Data Using Atom-Centered Potentials and Density Functional Theory

    Constructing Accurate Potential Energy Surfaces with Limited High-Level Data Using Atom-Centered Potentials and Density Functional Theory by Mahsa Nazemi-Ashani (21876936)

    Published 2025
    “…The effectiveness of the algorithm is demonstrated through its application to the HFCO and uracil molecules. …”
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  12. 3332
    Overall performance of the standard vs. extremised versions of each aggregation approach.

    Overall performance of the standard vs. extremised versions of each aggregation approach. by Marcellin Martinie (8763534)

    Published 2020
    “…The extremised MPW algorithm significantly outperforms both the standard and extremised versions of every other aggregation approach.…”
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  13. 3333
    Data_Sheet_2_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.ZIP

    Data_Sheet_2_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.ZIP by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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  14. 3334
    Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX

    Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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  15. 3335
    Data_Sheet_1_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.ZIP

    Data_Sheet_1_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.ZIP by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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  16. 3336
    Table_3_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX

    Table_3_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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  17. 3337
    Table_1_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX

    Table_1_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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  18. 3338
    Table_4_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX

    Table_4_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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  19. 3339
    Data_Sheet_1_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.DOCX

    Data_Sheet_1_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.DOCX by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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  20. 3340
    Data_Sheet_3_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.ZIP

    Data_Sheet_3_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.ZIP by María Santos-Merino (5722424)

    Published 2023
    “…<p>Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO<sub>2</sub> into products of interest such as fatty acids. …”
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