by Technical University of Munich (TUM) • 8 days ago
Researchers at TUM have advanced single-cell technology to analyze millions of individual cells, revealing how conditions like smoking and COVID-19 alter cell structures. They explored self-supervised learning methods to handle large datasets without pre-classified data, finding it enhances predictions about cell types and gene expression. The study indicates potential for developing virtual cell models to better understand cellular changes related to diseases, promising improvements in analysis efficiency.
