Abstract: Spatial transcriptomics technology fully leverages spatial location and gene expression information for spatial clustering tasks. However, existing spatial clustering methods primarily ...

Scientists at Duke-NUS Medical School have developed two powerful computational tools that could transform how researchers ...

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, driven by marked molecular and clinical heterogeneity and ...

The rapid expansion of high-throughput sequencing technologies has generated unprecedented amounts of multi-omics data, ...

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.

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 ...

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 ...

The graph database market, driven by AI, is growing at a rate of almost 25% annually. Graph databases support knowledge graphs, providing visual guidance for AI development. There are multiple ...

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

This study presents DeepTX, a valuable methodological tool that integrates mechanistic stochastic models with single-cell RNA sequencing data to infer transcriptional burst kinetics at genome scale.

This important study reveals that Excitatory Amino Acid Transporters play a role in chromatic information processing in the retina. The combination of (double) mutants, behavioral assays, ...