Lattice Paths for Persistent Diagrams

Moo K. Chung, Hernando Ombao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publicationInterpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data
PublisherSpringer International Publishing
Pages77-86
Number of pages10
ISBN (Print)9783030874438
DOIs
StatePublished - Sep 21 2021

Fingerprint

Dive into the research topics of 'Lattice Paths for Persistent Diagrams'. Together they form a unique fingerprint.

Cite this