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datasets available to search
ShareScore release 0.9.0
Dataset results
48 results for “contrastive learning”
Multimodal contrastive learning for spatial gene expression prediction using histology images
<p>we employed two human breast cancer datasets and one human cutaneous squamous cell carcinoma (cSCC) dataset.</p>
Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks
<p>The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).</p>
CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity
<p>Source code, datasets and outputs. </p>
Code for manuscript "Analysis of dynamic susceptibility contrast perfusion MRI using physics-informed deep learning"
<p>This is the processing code and in vivo statistics for the manuscript "Analysis of dynamic susceptibility contrast perfusion MRI using physics-informed deep learning".</p>
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
<p>The benchmark datasets used to evaluate Gaussian noise augmentation-based scRNA-seq contrastive learning (GsRCL) against scRNA-seq cell-type identification tasks.</p>
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.
Artificial Intelligent Accelerates the Learning Curve for Mastering Contrast-enhanced Ultrasound of Thyroid Nodules
ClinicalTrials.gov study NCT05982821. IPD Sharing: NO. Countries: 1. Publications: 19.
Graph Contrastive Learning with Adversarial Structure Refinement (GCL-ASR)
Open the record for dataset details and reuse information.
Change-Aware Sampling and Contrastive Learning for Satellite Images (Part 3)
<p>Part 3 of compressed CACo 1M data</p>
Change-Aware Sampling and Contrastive Learning for Satellite Images (Part 4)
<p>Part 4 of compressed CACo 1M data</p>
Change-Aware Sampling and Contrastive Learning for Satellite Images (Part 2)
<p>Part 2 of compressed CACo 1M data</p>
Change-Aware Sampling and Contrastive Learning for Satellite Images (Part 1)
<p>Part 1 of compressed CACo 1M data</p>
Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning
Open the record for dataset details and reuse information.
Unified Cross-modality Integration and Inference of Single-Cell Multiomics Data with Deep Contrastive Learning
Open the record for dataset details and reuse information.
Aligned Cross-modal Integration and Regulatory Heterogeneity Characterization of Single-Cell Multiomic Data with Deep Contrastive Learning
Open the record for dataset details and reuse information.
Contrastive learning-based histopathological feature infers molecular subtypes and clinical outcomes of breast cancer from unannotated whole slide images
<p>The breast cancer cohort came from the Changzhou Second People's Hospital (CZSPH) in Jiangsu, China. This cohort collected 91 FFPE WSIs from 90 breast cancer patients, including 15 recurrence cases within 5 years.</p>
SpaMask: Dual Masking Graph Autoencoder with Contrastive Learning for Spatial Transcriptomics
<p>Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. <strong>Masking mechanisms</strong>, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose <strong>SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis</strong>. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines <strong>Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL)</strong> modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on <strong>eight datasets from five different platforms</strong>. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.</p>
A Study to Learn How Gadoquatrane Moves Into, Through, and Out of the Body and How Safe it is in Children (From Birth to <18 Years), Who Will Undergo a Contrast Enhanced MRI (Quanti Pediatric)
ClinicalTrials.gov study NCT05915026. IPD Sharing: NO. Countries: 10. Publications: 0.
Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
ClinicalTrials.gov study NCT07166445. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Low-contrast Dose Liver CT Using Lean Body Weight Low Monoenergetic Images and Deep Learning-based Reconstruction
ClinicalTrials.gov study NCT04027556. IPD Sharing: NO. Countries: 1. Publications: 0.
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.