Spatial transcriptomics is revolutionizing modern biology, offering researchers an
unprecedented ability to unravel intricate gene expression patterns within tissues. From …

Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue
organization. We introduce a graph convolutional network with an attention and positive …

Metric multidimensional scaling is one of the classical methods for embedding data into low-
dimensional Euclidean space. It creates the low-dimensional embedding by approximately …

Identifying cell types and states remains a time-consuming and error-prone challenge for
spatial biology. While deep learning is increasingly used, it is difficult to generalize due to …

Tumor research is a fundamental focus of medical science, yet the intrinsic heterogeneity
and complexity of tumors present challenges in understanding their biological mechanisms …

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial
biology. While deep learning is increasingly used, it is difficult to generalize due to variability …

In an era where transcriptomics-based subtyping, phenotyping and mechanistic
understanding is increasingly being driven by state-of-the-art spatially resolved …

Recent advances in spatial transcriptomics (ST) technology allow researchers to
comprehensively measure gene expression patterns at the level of individual cells or even …

Spatial transcriptomics data provide insights into gene expression patterns within tissue
contexts, where identifying spatial domains with similar gene expression is crucial …