Drug addiction is a global public health crisis, and the design of antiaddiction drugs remains
a major challenge due to intricate mechanisms. Since experimental drug screening and …

Persistent homology (PH) is one of the most popular tools in topological data analysis (TDA),
while graph theory has had a significant impact on data science. Our earlier work introduced …

From flocking birds to schooling fish, organisms interact to form collective dynamics across
the natural world. Self-organization is present at smaller scales as well: cells interact and …

A primary challenge in understanding collective behavior is characterizing the
spatiotemporal dynamics of the group. We employ topological data analysis to explore the …

Filament-motor interactions inside cells play essential roles in many developmental as well
as other biological processes. For instance, actin-myosin interactions drive the emergence …

We present a novel method for identifying topological features of chromatin domains in live
cells using single-particle tracking and topological data analysis (TDA). By applying TDA to …

Mathematical models come in many forms across biological applications. In the case of
complex, spatial dynamics and pattern formation, stochastic models also face two main …

The complex behavior of many real-world systems depends on a network of both strong and
weak edges. Distinguishing between true weak edges and low-weight edges caused by …

Genome-wide association studies (GWAS) involving increasing sample sizes have identified
hundreds of genetic variants associated with complex diseases, such as type 2 diabetes …

Topological data analysis (TDA) uses persistent homology to quantify loops and higher-
dimensional holes in data, making it particularly relevant for examining the characteristics of …