A new study in Science Bulletin presents DVSTP, a deep learning system that integrates pathology images with spatial ...
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 ...
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 ...
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.4c04462. Averaged mass spectrum from the rat brain tissue with ...
At AACR 2024, we explored the poster hall to pick out the posters that would interest the BioTechniques reader and those we found delivered the most interesting or surprising findings. Get our ...
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