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251
datasets available to search
ShareScore release 0.7.1
Dataset results
251 results for “deep learning models”
Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)
ClinicalTrials.gov study NCT06088134. IPD Sharing: NO. Countries: 1. Publications: 1.
Deep Learning Models for Prediction of Intraoperative Hypotension Using Non-invasive Parameters
ClinicalTrials.gov study NCT05762237. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Deep Learning Model Detecting Pressure Injury
ClinicalTrials.gov study NCT06641258. IPD Sharing: NO. Countries: 1. Publications: 2.
The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging
ClinicalTrials.gov study NCT06864702. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
ClinicalTrials.gov study NCT07349901. IPD Sharing: NO. Countries: 1. Publications: 21.
Assessing Demographic Biases in Deep Learning Model for Fetal Growth Estimation in Clinical Practice. Patients Eligible for Inclusion Are Women with a Gestational Age Between 24-42 Weeks Undergoing a
ClinicalTrials.gov study NCT06314178. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis
ClinicalTrials.gov study NCT06760234. IPD Sharing: NO. Countries: 1. Publications: 1.
Risk Stratification of Hepatocarcinogenesis Using a Deep Learning Based Clinical, Biological and Ultrasound Model in High-risk Patients
ClinicalTrials.gov study NCT04802954. IPD Sharing: NO. Countries: 1. Publications: 14.
Data from: Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model
Open the record for dataset details and reuse information.
Datasets used for Multi-omics integration using Deep Learning and other state-of-the-art regression models
<p>This repository link contains the LIHC files that were downloaded using TCGA Assembler 2 and used in the publication for benchmarking DL and other state-of-the-art regression models.</p> <p>The contents are as follows.</p> <p>Gene level CNA , filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC__genome_wide_snp_6__GeneLevelCNA.txt">LIHC__genome_wide_snp_6__GeneLevelCNA.txt</a>"</p> <p>DNA Methylation data around 1500 bp around TSS (450K) , filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC_Methylation450__SingleValue__TSS1500__Both.txt">LIHC_Methylation450__SingleValue__TSS1500__Both.tx</a>t"</p> <p>RNASeq data, filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC_RNASeq__illuminahiseq_rnaseqv2__GeneExp.txt">LIHC_RNASeq__illuminahiseq_rnaseqv2__GeneExp.txt</a>"</p>
Training datasets and final models from paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation'
<p> See paper ''RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation' for an explanation of how the models and datasets were created.</p> <p>The images are extracted from the following larger datasets using the RootPainter software:</p> <p>Nodules: http://doi.org/10.5281/zenodo.3753603</p> <p>Biopores: http://doi.org/10.5281/zenodo.3753969</p> <p>Roots: http://doi.org/10.5281/zenodo.3527713</p> <p> </p>
Deep learning models to detect microsatellite instability in colorectal cancer from histological images
<p>This repository contains trained deep learning models (based on shufflenet) for detecting microsatellite instability (MSI) in histological images of colorectal cancer. Expected input is 512x512x3, output is categorical (MSI vs. not MSI).</p> <p>The models are provided as MAT (Matlab R2019a) files. All models are described in detail in the publication "Clinical-grade detection of microsatellite instability in colorectal cancer by deep learning: an international multicenter study"</p> <p>More information on the original model: <a href="https://www.mathworks.com/help/deeplearning/ref/shufflenet.html">https://www.mathworks.com/help/deeplearning/ref/shufflenet.html</a></p>
SpatPPI: a geometric deep learning model for predicting protein-protein interactions involving intrinsically disordered regions
Open the record for dataset details and reuse information.
Estimation of Ca2+ wet deposition in the Northern Hemisphere by use of CNN deep-learning model
<p>A dataset to estimate long-term and high-resolution gridded Ca2+ wet deposition across the Northern Hemisphere from 2001-2022.</p>
Coupling deep learning and physically-based hydrological models for monthly streamflow predictions
<p>Revision in journal Water Resources Research, Paper # <strong><span>2023WR035618R</span></strong></p>
3D-MSNet: A point cloud based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data
<p>Supplementary data of 3D-MSNet</p>
Saved Deep Learning Models for Recurrence Score Prediction
<p>Saved Deep Learning Models for Recurrence Score Prediction</p>
Conformation Database for Publication: Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
<p><strong>Conformation database</strong> for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction"</p> <ul> <li>DOI of Physica A publication: <a href="https://doi.org/10.1016/j.physa.2022.128395">https://doi.org/10.1016/j.physa.2022.128395</a></li> <li>GitHub source code: <a href="https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction">https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction</a></li> </ul> <p>This conformation database shows the distinct conformations of best-known and next best energies:</p> <p>├── <strong>20merA</strong><br> │ ├── <strong>20merA_E8_set</strong><br> │ ├── <strong>20merA_E9_set</strong><br> │ ├── confs_20merA_E8.txt<br> │ └── confs_20merA_E9.txt<br> ├── <strong>20merB</strong><br> │ ├── <strong>20merB_E10_set</strong><br> │ ├── <strong>20merB_E9_set</strong><br> │ ├── confs_20merB_E10.txt<br> │ └── confs_20merB_E9.txt<br> ├── <strong>24mer</strong><br> │ ├── <strong>24mer_E8_set</strong><br> │ ├── <strong>24mer_E9_set</strong><br> │ ├── confs_24mer_E8.txt<br> │ └── confs_24mer_E9.txt<br> ├── <strong>25mer</strong><br> │ ├── <strong>25mer_E7_set</strong><br> │ ├── <strong>25mer_E8_set</strong><br> │ ├── confs_25mer_E7.txt<br> │ └── confs_25mer_E8.txt<br> ├── <strong>36mer</strong><br> │ ├── <strong>36mer_E13_set</strong><br> │ ├── <strong>36mer_E14_set</strong><br> │ ├── confs_36mer_E13.txt<br> │ └── confs_36mer_E14.txt<br> ├── <strong>48mer</strong><br> │ ├── <strong>48mer_E22_set</strong><br> │ ├── <strong>48mer_E23_set</strong><br> │ ├── confs_48mer_E22.txt<br> │ └── confs_48mer_E23.txt<br> └── <strong>50mer</strong><br> ├── <strong>50mer_E20_set</strong><br> ├── <strong>50mer_E21_set</strong><br> ├── confs_50mer_E20.txt<br> └── confs_50mer_E21.txt</p>
Evaluating Deep Learning-based Vulnerability Detection Models on Realistic Datasets
<p>This package contains all the data we used for our experiments.<br> </p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 6
<p>Future projections of precipitation by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</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.