Lattice Paths for Persistent Diagrams
Machine Learning
2021-12-13 v5 Machine Learning
Biomolecules
Abstract
Persistent homology has undergone significant development in recent years. However, one outstanding challenge is to build a coherent statistical inference procedure on persistent diagrams. In this paper, we first present a new lattice path representation for persistent diagrams. We then develop a new exact statistical inference procedure for lattice paths via combinatorial enumerations. The lattice path method is applied to the topological characterization of the protein structures of the COVID-19 virus. We demonstrate that there are topological changes during the conformational change of spike proteins.
Cite
@article{arxiv.2105.00351,
title = {Lattice Paths for Persistent Diagrams},
author = {Moo K. Chung and Hernando Ombao},
journal= {arXiv preprint arXiv:2105.00351},
year = {2021}
}