Rubén J. Sánchez-García

The paper quantifies gene expression heterogeneity in single-cell sequencing data and shows it relates to cell type, decomposing it into inter- and intra-cluster components.

This paper uses persistent homology to analyze bone microstructure in non-linear microscopy images, showing significant differences between pathological and healthy samples.

This paper develops a framework to understand lineage architecture and stem cell identity using network science, showing that lineage structures arise from homeostasis and that stemness is context-dependent.

The paper uses a discrete Morse theory clustering algorithm on a TDA network of gene expression data to identify nine molecular phenotypes associated with asthma and glucocorticoid receptor signaling differences.

This study develops a mathematical framework to analyze how signaling network structures influence noise processing and finds that sparse, directional networks are better at distinguishing signals from noise.