Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

Convolutional neural networks (CNNs) have played an important role in medical imaging—
from diagnostics to research to data integration. This has allowed clinicians to plan …

In computer-aided drug discovery (CADD), virtual screening (VS) is used for comparing a
library of compounds against known active ligands to identify the drug candidates that are …

This work introduces the Topological CNN (TCNN), which encompasses several
topologically defined convolutional methods. Manifolds with important relationships to the …

Motivation Topological methods have recently emerged as a reliable and interpretable
framework for extracting information from high-dimensional data, leading to the creation of a …

Mass Spectrometry Imaging (MSI) is a technique used to identify the spatial distribution of
molecules in tissues. An MSI experiment results in large amounts of high dimensional data …

The Euler characteristic transform (ECT) is a simple to define yet powerful representation of
shape. The idea is to encode an embedded shape by tracking how the Euler characteristic …

Spatiotemporal models that account for heterogeneity within microbial communities rely on
single-cell data for calibration and validation. Such data, commonly collected via microscopy …

Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields
such as drug discovery, materials design, separations, biological systems, and reaction …

Background Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that
can lead to cirrhosis and hepatic decompensation. However, predicting future outcomes in …