Lifesaving AI technology tracks how cells respond to drug treatments
Single-cell gene sequencing has changed the way scientists understand life at the smallest level. It lets researchers study how each cell behaves, especially when cells face challenges like disease or drug treatments. But while the technology provides great detail, it also creates a huge amount of noise in the data. This noise often hides important biological signals, making it hard to draw accurate conclusions. One of the most promising solutions to this problem, published in the scientific journal Nature Methods, comes from a new method called scNET. Developed by researchers at a university in Israel, this system blends two powerful tools: single-cell RNA sequencing and protein–protein interaction networks. By combining these, scNET can give you a much clearer picture of how genes and cells interact across different conditions. The noise problem in single-cell data Single-cell RNA sequencing, or scRNA-seq, lets you examine gene activity inside individual cells. Unlike older methods that average gene activity across millions of cells, this newer approach allows scientists to spot unique behaviors of different cells—even when they belong to the …