Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-
seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with …

Glioblastoma (GBM) is a particularly devastating tumor with a median survival of about 16
months. Recent research has revealed novel insights into the outstanding heterogeneity of …

Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a
tissue slice while recording the two-dimensional (2D) coordinates of each spot. We …

Optimal integration of transcriptomics data and associated spatial information is essential
towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out …

Recent technological advancements have enabled spatially resolved transcriptomic profiling
but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific …

The idea that tumour microenvironment (TME) is organised in a spatial manner will not
surprise many cancer biologists; however, systematically capturing spatial architecture of …

Spatial transcriptomics is a newly emerging field that enables high‐throughput investigation
of the spatial localization of transcripts and related analyses in various applications for …

It has been almost two decades since the first link between microRNAs and cancer was
established. In the ensuing years, this abundant class of short noncoding regulatory RNAs …

Solid tumours comprise many different cell types organized in spatially structured
arrangements, with substantial intratumour and intertumour heterogeneity. Advances in …