KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant NumberCRG
Permanent link to this recordhttp://hdl.handle.net/10754/669125
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AbstractPersistent 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.
CitationChung, M. K., & Ombao, H. (2021). Lattice Paths for Persistent Diagrams. Lecture Notes in Computer Science, 77–86. doi:10.1007/978-3-030-87444-5_8
SponsorsThe illustration of COVID-19 virus (Fig. 1 left) is provided by Alissa Eckert and Dan Higgins of Disease Control and Prevention (CDC), US. The proteins 6VXX and 6VYB are provided by Alexander Walls of University of Washington. The protein 6JX7 is provided by Tzu-Jing Yang of National Taiwan University. Figure 2-left is modified from an image in Wikipedia. This study is supported by NIH EB022856 and EB028753, NSF MDS-2010778, and CRG from KAUST.
PublisherSpringer International Publishing
Conference/Event name4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020 and 1st International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021