Lattice Paths for Persistent Diagrams.

Lattice Paths for Persistent Diagrams.
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DOI:
10.1007/978-3-030-87444-5_8
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发表时间:
2021
期刊:
Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data : 4th International Workshop, iMIMIC 2021, and 1st International Workshop, TDA4MedicalData 2021, He...
影响因子:
--
通讯作者:
Ombao H
Ombao H
中科院分区:
其他
文献类型:
--
作者:
Chung MK;Ombao H

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近年来,持久同源性得到了显着的发展。然而,一个突出的挑战是在持久图上构建连贯的统计推断过程。在本文中,我们首先提出了持久图的新格路径表示。然后,我们通过组合枚举为格路径开发一种新的精确统计推断程序。格路径法应用于COVID-19病毒蛋白质结构的拓扑表征。我们证明了刺突蛋白构象变化期间存在拓扑变化。
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.
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