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.

Illustration of a spatial imprint of captured transcripts by Nova-ST, along with the localization of binned clustering, for a coronal section of the mouse brain. The illustration below the brain ...

Mapping biological networks in lung adenocarcinoma using transcriptomic analysis to identify prognostic biomarkers and therapeutic targets.

Conventional transcriptomic techniques have revealed much about gene expression at the population and single-cell level—but they overlook one crucial factor: spatial context. In musculoskeletal ...

Race-specific survival prediction models for de novo metastatic breast cancer using machine learning. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not ...

Spatial transcriptomics is a technique that provides information about gene expression patterns within intact tissues. This technology employs various methodologies, including in situ sequencing (ISS) ...

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 ...

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 eBook cuts through the complexity of spatial techniques, featuring a series of interviews with pioneers in the field, and peer-reviewed research from the Taylor & Francis journal portfolio. This ...