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 …

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but
does not capture their spatial distribution nor reveal local networks of intercellular …

The human cerebral cortex has tremendous cellular diversity. How different cell types are
organized in the human cortex and how cellular organization varies across species remain …

Multi‐omics usually refers to the crossover application of multiple high‐throughput screening
technologies represented by genomics, transcriptomics, single‐cell transcriptomics …

Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early
stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high …

Multi-omics approaches have become a reality in both large genomics projects and small
laboratories. However, the multi-omics research community still faces a number of issues …

Recent technological advances have enabled spatially resolved measurements of
expression profiles for hundreds to thousands of genes in fixed tissues at single-cell …

Clustering methods have been widely used in single-cell RNA-seq data for investigating
tumor heterogeneity. Since traditional clustering methods fail to capture the high-dimension …

Glioblastoma (GBM) is the most invasive and deadliest brain cancer in adults. Its inherent
heterogeneity has been designated as the main cause of treatment failure. Thus, a deeper …

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome
sequencing methods, are increasingly used to study cancer and related diseases. Cell …