A novel spatial transcriptomics atlas developed by Northwestern Medicine scientists may improve the understanding of niche cellular interactions in the gastrointestinal tract that promote the ...
Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms ...
Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...
aDivision of Rheumatology, Department of Internal Medicine, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, 510630, China bDepartment of Endocrinology and Metabolism, ...
This repository contains code for the SpatialDIVA method, associated preprocessing, and evaluations performed in the manuscript - "Multi-modal disentanglement of spatial transcriptomics and ...
Artificial intelligence (AI) has become a common tool for bioinformatics, with hundreds of methods published in recent years. Due to the training data demands of deep-learning algorithms, ...
This study addresses a critical challenge in spatial multi-omics: the effective integration of heterogeneous molecular modalities within complex tissue environments. By introducing SpaDDM, a ...
This repository contains the code of the paper "DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images". Authors: Kalin Nonchev, Sebastian Dawo, Karina ...
Abstract: The majority of spatial transcriptomics datasets are characterized by low resolution, wherein each spot generally encompasses multiple cells. This limitation poses challenges for exploring ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results