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9 results for “geometric framework”
P2PXML Dataset: Deep Geometric Framework to Predict Antibody-Antigen Binding Affinity
<p>In drug development, the efficacy of an antibody depends on how the antibody interacts with the target antigen. The strength of these interactions indicates how successful an antibody is in neutralizing an antigen. Therefore, the strength, measured by “binding affinity”, is a critical aspect of antibody engineering. In theory, the higher the binding affinity, the higher the chances are that the antibody is successful against the target antigen. Currently, techniques such as molecular docking and molecular dynamics are utilized in quantifying the binding affinity. However, owing to the computational complexity of the aforementioned techniques, running simulations for large antibodies/antigens remains a daunting task. Despite the commendable improvements in deep learning-based binding affinity prediction, such approaches are highly dependent on the quality of the antibody-antigen structures and they tend to overlook the importance of capturing the evolutionary details of proteins upon mutation. Further, most of the existing datasets for the task only include antibody-antigen pairs related to one antigen variant and, thus, are not suitable for developing comprehensive data-driven approaches. To circumvent the said complexities, we first curate the largest and most generalized datasets for antibody-antigen binding affinity prediction, consisting of both protein sequences and structures. Subsequently, we propose a deep geometric neural network comprising a structure-based model and a sequence-based model that considers both atomistic and evolutionary details when predicting the binding affinity. The proposed framework exhibited a 10% improvement in mean absolute error compared to the state-of-the-art models while showing a strong correlation between the predictions and target values. We release the datasets and code publicly https://drug-discovery-entc.github.io/p2pxml/ to support the development of antibody-antigen binding affinity prediction frameworks for the benefit of science and society. </p>
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience
<p><strong>Supplementary Data</strong> </p> <ol> <li><strong>Supplementary Data 1</strong> contains the input (non-watertight) surface meshes of the block (shown in Figure 2a) reconstructed within the context of the EPFL-KAUST collaboration, and the corresponding output (watertight) meshes generated by Ultraliser.</li> <li><strong>Supplementary Data 2 </strong>contains a set of 20 non-watertight meshes that were randomly selected from the block shown in <strong>Supplementary Figure S54</strong> and another set of the their watertight counterparts.</li> <li><strong>Supplementary Data 3</strong> contains a set of 25 neuronal morphologies with different morphological types and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 4</strong> contains a set of 25 synthetic astroglial morphologies 15 and their corresponding watertight meshes.</li> <li><strong>Supplementary Data 5</strong> contains the vascular morphology (shown in <strong>Supplementary Fig. S83</strong>) and a corresponding multi-partitioned watertight mesh.</li> <li><strong>Supplementary Data 6</strong> contains the datasets used for the comparative analysis shown in <strong>Supplementary Section 13</strong>.<br> <br> Neuronal, astrocytic and vascular morphologies are stored in SWC, H5 and VMV file formats respectively. The file structures of the SWC and VMV formats are publicly available online. The H5 files of the complete astrocyte cells can be made available from corresponding authors upon request. All the surface meshes are stored in Wavefront OBJ files. Additional STL meshes are generated to be used for TetGen to create corresponding tetrahedral meshes. All the input and generated data files are publicly available on Zenodo (10.5281/zenodo.7105941).</li> </ol> <p><strong>Data Sources</strong> </p> <ol> <li>Cellular and subcellular NGV meshes segmented from the volume shown in Figure 2 are provided by the collaborating co-authors affiliated with KAUST.</li> <li>Neuronal meshes shown in Figure 3, Supplementary Figures S55 - S75 and Supplementary Figures S85 are publicly available from the MICrONS program.</li> <li>Neuronal morphologies shown in Figure 4, Supplementary Figures S80 - S81 and Supplementary Figure S86 are publicly available from NeuroMorpho.Org.</li> <li>Astrocytic morphologies (Figure 5 and Supplementary Figure S82) are provided by Eleftherios Zisis.</li> <li>Vascular morphologies (rat’s cerebral microvasculature) shown in Figure 6 and Supplementary Figures S83 - S84 are courtesy of Bruno Weber, University of Zürich (UZH).</li> <li>The vascular morphology of the arterial arborizations shown in Supplementary Figure S88 is available from the Brain Vasculature (BraVa) database (cng.gmu.edu/brava).</li> </ol>
Research data for "Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining"
<p>This dataset supports the paper: "Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining", available at the following DOI: 10.1039/d0sc03287e.</p> <p>The cleaned-up, coarse-grained, and re-scaled structures are provided here in both XYZ and CIF format. The data presented in the journal publication is also included; namely, MDS coordinates, T densities, and A-site heterogeneities for each structure in the dataset. T densities -- defined as: metals per unit volume (nm-3) -- are calculated using the re-scaled structures.</p>
Dataset for "Analytic High-order Geometric Derivatives with Polarizable Embedding in a Response Function Framework"
<p>This dataset contains data for the article "Analytic High-order Geometric Derivatives with Polarizable Embedding in a Response Function Framework"</p>
Assemblies generated in the manuscript "Geometric deep learning framework for de novo genome assembly"
<p>Assemblies evaluated in the manuscript "Geometric deep learning framework for de novo genome assembly". All the assemblies were generated by us, except CHM13.ONT.Flye-2.9.fa.gz which was generated by <a href="https://www.nature.com/articles/s41587-019-0072-8">Kolmogorov et al. (2019)</a>.</p>
Data from: The geometric framework for nutrition reveals interactions between protein and carbohydrate during larval growth in honey bees
Open the record for dataset details and reuse information.
Research data for "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks"
<p>This dataset supports the paper: "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks".</p> <p>The coarse-grained scaled and unscaled structures are provided here in CIF format. The code introduced in this work is subject to continuing development (and can be found at: https://github.com/tcnicholas/coarse-graining), therefore we include here the version of the code used for this paper alongside the Python analysis scripts.</p> <p>The original unprocessed CIFs were extracted from the Cambridge Structural Database (CSD).</p>
Accuracy of Standard and Geometric Pattern-Assisted Digital Scanning for Full-Arch Implant Prosthesis Frameworks
ClinicalTrials.gov study NCT07065487. IPD Sharing: YES. Countries: 1. Publications: 0.
Geometric Framework for nutrition on Sucrose Taste Sensitization in Drosophila
GEO Series GSE217385. Drosophila melanogaster. 10 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.