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53
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Dataset results
53 results for “reconstruction algorithms”
single-cell RNAseq data (data set 13) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset13) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor11 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 10) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset10) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor8 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 6) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset6) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor4 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: <a href="https://github.com/sysbiolux/scFASTCORMICS">https://github.com/sysbiolux/scFASTCORMICS</a></p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 2) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset2) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from normal mucosa samples downloaded from the GEO website (<strong>GSE81861). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021
<p>Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (D, H and Z) in 2021. This reconstruction method has been proved to be effective.</p>
Accurate real space iterative reconstruction (RESIRE) algorithm for tomography
<p>This is the code and dataset for the paper: Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Three dataset are provided in this deposit:</p> <p>1. the simulation of vesicle model</p> <p>2. the biomineral data</p> <p>3. Amorphous Ta thinfilm data</p> <p>The algorithm are written in matlab and cuda code. Users can either use the cpu or gpu versions.</p> <p>1. The cpu code can only do tomography reconstructions with single tilt axis (say y-axis)</p> <p>2. The gpu code can do with multiple tilt axes, say ZYX<br> To use the gpu cude, you need to download and install CUDA toolkit from the NVIDA website https://developer.nvidia.com/cuda-downloads. Then, open Matlab and compile cuda functions, for example:<br> mexcuda -R2018a RT3_1GPU.cu<br> The mexw64 cuda files attached in this folder are precompiled for windows, cuda version = 11.x</p> <p>If you use the code or the data, please cite our paper:<br> Pham, M., Yuan, Y., Rana, A. et al. Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Sci Rep 13, 5624 (2023). https://doi.org/10.1038/s41598-023-31124-7</p> <p>If you have question, feel free to email us at minhrose@ucla.edu</p>
Preclinical Development of a 3D Bili-MRI Reconstruction Tool and an Artificial Intelligence Algorithm to Assist Endoscopists in Performing Endoscopic Retrograde Cholangiopancreatography (ERCP)
ClinicalTrials.gov study NCT07401485. IPD Sharing: NO. Countries: 1. Publications: 0.
Detection and Volumetry of Pulmonary Nodules on Ultra-low Dose Chest CT Scan With Deeplearning Image Reconstruction Algorithm (DLIR)
ClinicalTrials.gov study NCT04482114. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A New Deep-learning Based Artificial Intelligence Iterative Reconstruction (AIIR) Algorithm in Low-dose Liver CT
ClinicalTrials.gov study NCT05550012. IPD Sharing: Not stated. Countries: 1. Publications: 0.
PET/MRI Artificial Intelligence Reconstruction Algorithm AIR Recon DL Image Quality Evaluation and Clinical Study
ClinicalTrials.gov study NCT06856096. IPD Sharing: NO. Countries: 1. Publications: 0.
Detection of Urinary Stones on ULDCT With Deep-learning Image Reconstruction Algorithm
ClinicalTrials.gov study NCT04490343. IPD Sharing: NO. Countries: 1. Publications: 0.
In Vivo Depiction Of Bone Vascularization With UHR-CT CT And Deep Learning Algorithm Reconstruction: A Preliminary Study
ClinicalTrials.gov study NCT05628792. IPD Sharing: NO. Countries: 0. Publications: 0.
Dataset related to the article "Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm"
<p>This record contains raw data related to the article “Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm”</p> <p>Abstract</p> <p><strong>Background: </strong>Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium enhancement sequences (2D-SSLGE). The aim of the study is to assess the feasibility and comparison of 2D-MSLGE reconstructed with artificial intelligence reconstruction deep learning noise reduction (NR) algorithm compared to standard 2D-SSLGE in consecutive patients with ischemic cardiomyopathy (ICM).</p> <p><strong>Methods: </strong>Fifty-seven patients with known ICM referred for a clinically indicated CMR were enrolled in this study. 2D-MSLGE were reconstructed using a growing level of NR (0%,25%,50%,75%and 100%). Subjective image quality, signal to noise ratio (SNR) and contrast to noise ratio (CNR) were evaluated in each dataset and compared to standard 2D-SSLGE. Moreover, diagnostic accuracy, LGE mass and scan time were compared between 2D-MSLGE with NR and 2D-SSLGE.</p> <p><strong>Results: </strong>The application of NR reconstruction ≥50% to 2D-MSLGE provided better subjective image quality, CNR and SNR compared to 2D-SSLGE (p < 0.01). The best compromise in terms of subjective and objective image quality was observed for values of 2D-MSLGE 75%, while no differences were found in terms of LGE quantification between 2D-MSLGE versus 2D-SSLGE, regardless the NR applied. The sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 2D-MSLGE NR 75% were 87.77%,96.27%,96.13%,88.16% and 94.22%, respectively. Time of acquisition of 2D-MSLGE was significantly shorter compared to 2D-SSLGE (p < 0.01).</p> <p><strong>Conclusion: </strong>When compared to standard 2D-SSLGE, the application of NR reconstruction to 2D-MSLGE provides superior image quality with similar diagnostic accuracy.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.