The human brain is a fascinating and complex organ that supports numerous sophisticated behaviors and abilities that are observed in no other animal species. For centuries, scientists have been trying ...

Modern machine learning approaches have shown remarkable success in extracting patterns from high-dimensional biological data. However, when applied to spatial transcriptomics, these methods face ...

The evolution of single-cell sequencing and spatial omics technologies has revolutionised our ability to study tissues and diseases with unprecedented resolution. It has long been recognised that gene ...

Transcriptomics represents a critical discipline in cancer research, enabling comprehensive mapping of gene expression profiles and the identification of fusion genes implicated in tumor development.

Minimally invasive lumbar decompression (MILD), also known as percutaneous image-guided lumbar decompression (PILD), is a minimally invasive treatment option for some people with spinal stenosis in ...

Do you want to generate spatial transcriptomics data using your H&E images? We introduce DeepSpot, a novel deep-learning model that predicts spatial transcriptomics from H&E images. DeepSpot employs a ...

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 ...

The liver is remarkable for its ability to regenerate after injury, yet when this process fails, acute liver failure (ALF) carries devastating outcomes. Traditional research methods, reliant on bulk ...

A pilot program in six states will use a tactic employed by private insurers that has been heavily criticized for delaying and denying medical care. By Reed Abelson and Teddy Rosenbluth Like millions ...

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

Transcriptomics Data Analysis (TDA) is a live, instructor-led training program designed to introduce participants to best practices in the analysis of high-throughput RNA sequencing (RNA-seq) data.