Bioptimus, a global AI company building the world's first world model for biology, today announced the launch of its Spatial ...

A wave of spatial transcriptomics studies has produced gene-expression atlases that span entire organs and whole organisms, ...

Spatial transcriptomics offers a revolutionary approach to understanding the tumor microenvironment (TME) in solid tumors by preserving the spatial context of gene expression, which is crucial for ...

This figure shows how the STAIG framework can successfully identify spatial domains by integrating image processing and contrastive learning to analyze spatial transcriptomics data effectively.

Biological tissues are made up of different cell types arranged in specific patterns, which are essential to their proper functioning. Understanding these spatial arrangements is important when ...

Foundation models (FMs), which are deep learning models pretrained on large-scale data and applied to diverse downstream ...

Tumors contain many different types of cells organized in complex spatial patterns that can influence how the disease progresses. Because of this, it is hard to predict how a tumor will develop and ...

Spatial transcriptomics is a cutting-edge technique that characterizes gene expression within sections of tissue, such as heart, skin or liver tissue. These snapshots provide insights into how spatial ...