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Showing 1 - 20 results of 26 for search '(( binary basic robust optimization algorithm ) OR ( library based small optimization algorithm ))', query time: 0.38s Refine Results
  1. 1
    iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules

    iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules by Denis N. Prada Gori (5798651)

    Published 2022
    “…Here, two open-source in-house methodologies for clustering of small molecules are presented: iterative Random subspace Principal Component Analysis clustering (iRaPCA), an iterative approach based on feature bagging, dimensionality reduction, and K-means optimization; and Silhouette Optimized Molecular Clustering (SOMoC), which combines molecular fingerprints with the Uniform Manifold Approximation and Projection (UMAP) and Gaussian Mixture Model algorithm (GMM). …”
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  2. 2
    iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules

    iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules by Denis N. Prada Gori (5798651)

    Published 2022
    “…Here, two open-source in-house methodologies for clustering of small molecules are presented: iterative Random subspace Principal Component Analysis clustering (iRaPCA), an iterative approach based on feature bagging, dimensionality reduction, and K-means optimization; and Silhouette Optimized Molecular Clustering (SOMoC), which combines molecular fingerprints with the Uniform Manifold Approximation and Projection (UMAP) and Gaussian Mixture Model algorithm (GMM). …”
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  3. 3
    iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules

    iRaPCA and SOMoC: Development and Validation of Web Applications for New Approaches for the Clustering of Small Molecules by Denis N. Prada Gori (5798651)

    Published 2022
    “…Here, two open-source in-house methodologies for clustering of small molecules are presented: iterative Random subspace Principal Component Analysis clustering (iRaPCA), an iterative approach based on feature bagging, dimensionality reduction, and K-means optimization; and Silhouette Optimized Molecular Clustering (SOMoC), which combines molecular fingerprints with the Uniform Manifold Approximation and Projection (UMAP) and Gaussian Mixture Model algorithm (GMM). …”
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  4. 4
    DataSheet1_Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.docx

    DataSheet1_Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.docx by Sergei Evteev (18294157)

    Published 2024
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  5. 5
    Acceleration of Inverse Molecular Design by Using Predictive Techniques

    Acceleration of Inverse Molecular Design by Using Predictive Techniques by Jos L. Teunissen (1911856)

    Published 2019
    “…This study addresses one of the most important drawbacks inherently related to molecular searches in chemical compound space by greedy algorithms such as Best First Search and Genetic Algorithm, i.e., the large computational cost required to optimize one or more quantum-chemical properties. …”
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  6. 6
    Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential

    Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential by Fengyang Han (14613218)

    Published 2023
    “…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …”
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  7. 7
    Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential

    Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential by Fengyang Han (14613218)

    Published 2023
    “…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …”
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  8. 8
    Image_13_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_13_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  9. 9
    Image_2_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_2_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  10. 10
    Image_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  11. 11
    Image_6_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_6_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  12. 12
    Image_10_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_10_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  13. 13
    Data_Sheet_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.pdf

    Data_Sheet_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.pdf by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  14. 14
    Image_12_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_12_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  15. 15
    Image_3_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_3_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  16. 16
    Data_Sheet_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.zip

    Data_Sheet_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.zip by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  17. 17
    Image_4_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_4_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  18. 18
    Image_9_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_9_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  19. 19
    Image_11_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_11_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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  20. 20
    Image_5_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG

    Image_5_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

    Published 2022
    “…This is partly due to the methodological challenge of accurately measuring rare and small WMH with automated segmentation programs. In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”
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