Evidence map›Paper›PMID 39650607›Full record

ArticleArXiv2024

Multisacle Jones Polynomial and Persistent Jones Polynomial for Knot Data Analysis.

Ruzhi Song, Fengling Li, Jie Wu, Fengchun Lei, Guo-Wei Wei

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PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. 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

5 authors.

Ruzhi SongSchool of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
Fengling LiSchool of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
Jie WuBeijing Institute of Mathematical Sciences and Applications, Beijing 101408, China.
Fengchun LeiSchool of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
Guo-Wei WeiDepartment of Mathematics, Michigan State University, MI 48824, USA.

Funding

AI-based platform for predicting emerging vaccine-escape variants and designing mutation-proof antibodiesR01AI164266 · NIAID · UNIVERSITY OF GEORGIA · PI Guowei Wei, YONG-HUI ZHENG · 2022 to 2026
$2.7M
Discovery-Driven Mathematics and Artificial Intelligence for Biosciences and Drug DiscoveryR35GM148196 · NIGMS · UNIVERSITY OF GEORGIA · PI Guowei Wei · 2023 to 2026
$1.5M
Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impactsR01GM126189 · NIGMS · MICHIGAN STATE UNIVERSITY · PI WEI, GUOWEI · 2018 to 2021
$1.4M
NIAID NIH HHS R01 AI164266NIGMS NIH HHS R01 GM126189NIGMS NIH HHS R35 GM148196
6 · The paper itself

Abstract

Many structures in science, engineering, and art can be viewed as curves in 3-space. The entanglement of these curves plays a crucial role in determining the functionality and physical properties of materials. Many concepts in knot theory provide theoretical tools to explore the complexity and entanglement of curves in 3-space. However, classical knot theory primarily focuses on global topological properties and lacks the consideration of local structural information, which is critical in practical applications. In this work, two localized models based on the Jones polynomial, namely the multiscale Jones polynomial and the persistent Jones polynomial, are proposed. The stability of these models, especially the insensitivity of the multiscale and persistent Jones polynomial models to small perturbations in curve collections, is analyzed, thus ensuring their robustness for real-world applications.

Indexed as

57K1492C10curve data analysisJones polynomialKnot data analysislocalizationprotein flexibilitystability

Identifiers

PMID39650607
PMCPMC11623711

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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.