Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …

Artificial intelligence (AI) based drug design has demonstrated great potential to
fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug …

Hypergraphs are useful mathematical models for describing complex relationships among
members of a structured graph, while hyperdigraphs serve as a generalization that can …

Recently various types of topological Laplacians have been studied from the perspective of
data analysis. The spectral theory of these Laplacians has significantly extended the scope …

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in
tissues, resulting in numerous biological discoveries. Various computational methods have …

Molecular descriptors are essential to quantitative structure activity/property relationship
(QSAR/QSPR) models and machine learning models. Here we propose persistent path …

Abstract The Delaunay filtration D.(X) of a point cloud X⊂ ℝd is a central tool of
computational topology. Its use is justified by the topological equivalence of D.(X) and the …

Persistent topological Laplacians constitute a new class of tools in topological data analysis
(TDA), motivated by the necessity to address challenges encountered in persistent …

Abstract The Euler Curve Transform (ECT) of Turner et al. is a complete invariant of an
embedded simplicial complex, which is amenable to statistical analysis. We generalize the …