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: Spatial transcriptomics (ST) are emerging technologies that reveal spatial distributions of gene expressions within tissues, serving as important ways to uncover biological insights. However ...

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, ...

Abstract: Spatial transcriptomics is an emerging technology that allows for analysis of cellular and molecular heterogeneity at spatial resolution. The accurate identification of pathological regions ...

Biological systems are inherently three-dimensional—tissues form intricate layers, networks, and architectures where cells interact in ways that extend far beyond a flat plane. To capture the true ...

We introduce a novel deep learning framework, SpaGene, which integrates scRNA-seq data and spatial transcriptomics data through an encoder-decoder architecture with translators, and discriminators, ...

Spatial transcriptomics enables researchers to measure gene expression in tissue sections while preserving their spatial organisation. Unlike traditional RNA sequencing, which requires dissociating ...

The tumor microenvironment (TME) of invasive lobular carcinoma (ILC) remains largely unexplored despite its clinical relevance and distinct biology. Using spatial transcriptomics, we reveal insights ...

This included single-cell transcriptomics, spatial transcriptomics, NanoString spatial proteomics, and publicly available bulk RNA sequencing (RNA-seq) datasets. These datasets were used to generate a ...