TY - GEN
T1 - Lattice Paths for Persistent Diagrams
AU - Chung, Moo K.
AU - Ombao, Hernando
N1 - KAUST Repository Item: Exported on 2021-10-05
Acknowledged KAUST grant number(s): CRG
Acknowledgements: The 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.
PY - 2021/9/21
Y1 - 2021/9/21
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/669125
UR - https://link.springer.com/10.1007/978-3-030-87444-5_8
UR - http://www.scopus.com/inward/record.url?scp=85115881366&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87444-5_8
DO - 10.1007/978-3-030-87444-5_8
M3 - Conference contribution
C2 - 34993529
SN - 9783030874438
SP - 77
EP - 86
BT - Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data
PB - Springer International Publishing
ER -