Researchers from several Parisian institutions have worked together to develop a non-destructive approach to study how unicellular organisms respond to stress, focusing on cell-to-cell differences.

New model extracts stiffness and fluidity from AFM data in minutes, enabling fast, accurate mechanical characterization of living cells at single-cell resolution. (Nanowerk Spotlight) Cells are not ...

The rapid advancement of spatial and single-cell omics technologies has revolutionized molecular biosciences by enabling high-resolution profiling of gene ...

The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions via a process called cell fate determination. The fate of individual cells, ...

Researchers at City of Hope, a cancer research and treatment organization, and the University of California, Berkeley, have ...

Scientists have known for more than a century that a single-celled organism with no nerve cells—much less a brain—can behave ...

Researchers at the Josep Carreras Leukaemia Research Institute have demonstrated that combining data from different origins enables a more precise characterization of cell type's diversity into ...

An NIH-funded study unveils scSurvival, a machine learning tool that predicts cancer outcomes using single-cell data. It ...

STANFORD, California, USA, 24 June 2025 – In a comprehensive Genomic Press interview, Stanford University researcher Eric Sun reveals how machine learning is revolutionizing our understanding of brain ...

Oregon Health & Science University researchers have developed a first-of-its-kind method to predict cancer patient survival using advanced molecular ...