Inference code for "Towards Spatial Transcriptomics-driven Pathology Foundation Models". This repository contains the basic model code as well as instructions to run inference on H&E and spatial ...

This study establishes a comprehensive framework that links histological image features to genotype, transcriptome, and chronological age in large-scale healthy tissue datasets, providing valuable ...

In the past Microsoft created many great tools, and one of these tools is Microsoft Expression Studio. Many users are concerned whether these old tools can work on modern operating systems, therefore ...

scBiG is a graph autoencoder network where the encoder based on multi-layer graph convolutional networks extracts high-order representations of cells and genes from the cell-gene bipartite graph, and ...

Characterizing cell identities and the effects of perturbations through the analysis of expression differences between groups of cells is a crucial challenge for single-cell omics. Conducting such a ...

Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data—e.g., given a ...

Single-cell transcriptional and epigenomics profiles have been applied in a variety of tissues and diseases for discovering new cell types, differentiation trajectories, and gene regulatory networks.

We present global cell-level TIL maps and 43 quantitative TIL spatial image features for 1,000 WSIs of The Cancer Genome Atlas patients with breast cancer. For more specific analysis, all the patients ...