Evidence map›Paper›PMID 34159224›Full record

ArticleArXiv2021

Lattice Paths for Persistent Diagrams.

Moo K Chung, Hernando Ombao

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Moo K ChungUniversity of Wisconsin, Madison, USA.
Hernando OmbaoKing Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Funding

BRAIN Initiative: Theories, Models and Methods for Analysis of Complex Data from the BrainR01EB022856 · NIBIB · UNIVERSITY OF WISCONSIN-MADISON · PI CHUNG, MOO K · 2016 to 2018
$1.1M
Dynamic manifold-valued time series model in functional brain imagingR01EB028753 · NIBIB · UNIVERSITY OF WISCONSIN-MADISON · PI CHUNG, MOO K · 2020 to 2022
$942k
NIBIB NIH HHS R01 EB022856NIBIB NIH HHS R01 EB028753
6 · The paper itself

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.

Identifiers

PMID34159224
PMCPMC8219103

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.