Yingfu Wu

Abstract Recent advances in spatially resolved transcriptomics have enabled large-scale measurement of gene expression while preserving spatial context, facilitating the investigation of spatial heterogeneity within tissues. In this study, we propose SpatialGEO, a geometric-aware deep learning framework that integrates gene expression profiles with spatial coordinates to generate biologically meaningful low-dimensional embeddings, enabling the dissection of complex tissue architectures.

Abstract With the widespread application of the CRISPR-Cas system in gene editing and related fields, the demand for detecting and classifying CRISPR-Cas systems in metagenomic data has continuously increased. The traditional classification of the CRISPR-Cas system mainly relies on identifying neighboring cas genes of repeats.