Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

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, ...

Spatial EcoTyper is a versatile framework for identifying spatially distinct multicellular communities, termed spatial ecotypes, from single-cell spatial transcriptomics data. In addition, it provides ...

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 ...

Layer 2/3 (L2/3) glutamatergic neurons are important sites of experience-dependent plasticity and learning in the mammalian cortex. Their properties vary continuously with cortical depth and depend ...

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 ...

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 ...

Abstract: Spatial transcriptomics data provides a unique opportunity to investigate both gene expression and spatial structure in tissues at the same time. However, incorporating spatial information ...