Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limited by incomplete molecular profiling, tissue loss, and probe failure. Here, we apply machine ...

Gastric cancer (GC) remains a global clinical challenge due to late diagnosis, high heterogeneity, and poor prognosis. Tumor stemness has emerged as a key factor driving tumor aggressiveness and ...

Master Scaling Single-Cell Biology. This free webinar series covers multi-omic data integration, spatial datasets, and new AI ...

A comprehensive review article titled “Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses,” published in Quantitative Biology, ...

Single-cell RNA-seq (scRNA-seq) has spent the past decade maturing into a foundational technology. Over that time, the technology has both laid the foundation for building cell atlases and allowed ...

A machine learning model developed by researchers at the Johns Hopkins Kimmel Cancer Center filters out the biological noise in liquid biopsy samples, helping clinicians better match therapies to ...

Much more difficult is learning to connect different types of stimuli or events, and predicting that one is linked to another. Such associative learning was most famously demonstrated when Ivan Pavlov ...

During early development, tissues and organs begin to form through the shifting, splitting, and growing of many thousands of cells. A team of researchers headed by MIT engineers has now developed a ...