Evidence map›Paper›PMID 32770408›Full record

ArticleBulletin of mathematical biology2020

The de Rham-Hodge Analysis and Modeling of Biomolecules.

Rundong Zhao, Menglun Wang, Jiahui Chen, Yiying Tong, Guo-Wei Wei

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Correlated clustering and projection for dimensionality reduction.Machine learning: science and technology · 2026
    Article
  2. Article
  3. PERSISTENT SHEAF LAPLACIANS.Foundations of data science (Springfield, Mo.) · 2025
    Article
  4. Article
  5. Hodge Decomposition of Single-Cell RNA Velocity.Journal of chemical information and modeling · 2024
    Article
  6. Combinatorial and Hodge Laplacians: Similarities and Differences.SIAM review. Society for Industrial and Applied Mathematics · 2024
    Article
  7. Article
  8. PERSISTENT PATH LAPLACIAN.Foundations of data science (Springfield, Mo.) · 2023
    Article
  9. Review
  10. Article
  11. Biomolecular Topology: Modelling and Analysis.Acta mathematica Sinica, English series · 2022
    Article
  12. EVOLUTIONARY DE RHAM-HODGE METHOD.Discrete and continuous dynamical systems. Series B · 2021
    Article
  13. HERMES: PERSISTENT SPECTRAL GRAPH SOFTWARE.Foundations of data science (Springfield, Mo.) · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rundong ZhaoDepartment of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
Menglun WangDepartment of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
Jiahui ChenDepartment of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.
Yiying TongDepartment of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA. ytong@msu.edu.
Guo-Wei WeiDepartment of Mathematics, Michigan State University, East Lansing, MI, 48824, USA. wei@math.msu.edu.ORCID 0000-0001-8132-5998

Funding

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
Division of Information and Intelligent Systems 1900473Division of Mathematical Sciences 1721024Division of Mathematical Sciences 1761320NIGMS NIH HHS 126189NIGMS NIH HHS R01 GM126189
6 · The paper itself

Abstract

Biological macromolecules have intricate structures that underpin their biological functions. Understanding their structure-function relationships remains a challenge due to their structural complexity and functional variability. Although de Rham-Hodge theory, a landmark of twentieth-century mathematics, has had a tremendous impact on mathematics and physics, it has not been devised for macromolecular modeling and analysis. In this work, we introduce de Rham-Hodge theory as a unified paradigm for analyzing the geometry, topology, flexibility, and Hodge mode analysis of biological macromolecules. Geometric characteristics and topological invariants are obtained either from the Helmholtz-Hodge decomposition of the scalar, vector, and/or tensor fields of a macromolecule or from the spectral analysis of various Laplace-de Rham operators defined on the molecular manifolds. We propose Laplace-de Rham spectral-based models for predicting macromolecular flexibility. We further construct a Laplace-de Rham-Helfrich operator for revealing cryo-EM natural frequencies. Extensive experiments are carried out to demonstrate that the proposed de Rham-Hodge paradigm is one of the most versatile tools for the multiscale modeling and analysis of biological macromolecules and subcellular organelles. Accurate, reliable, and topological structure-preserving algorithms for implementing discrete exterior calculus (DEC) have been developed to facilitate the aforementioned modeling and analysis of biological macromolecules. The proposed de Rham-Hodge paradigm has potential applications to subcellular organelles and the structure construction from medium- or low-resolution cryo-EM maps, and functional predictions from massive biomolecular datasets.

Indexed as

AlgorithmsModels, BiologicalCryoelectron MicroscopyMacromolecular SubstancesMathematical ConceptsOrganellesMacromolecular SubstancesAlgebraic topologyCryo-EM analysisDe Rham–Hodge theoryDifferential geometryMacromolecular flexibilityMacromolecular Hodge mode analysis

Identifiers

PMID32770408
PMCPMC8137271

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.