Evidence map›Paper›PMID 38617997›Full record

ArticleBiological imaging2024

Alignment of density maps in Wasserstein distance.

Amit Singer, Ruiyi Yang

Abstract read
In one paragraph

Article in Biological imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Subspace Method of Moments forSIAM journal on imaging sciences · 2026
    Article
  4. Article
  5. Article
  6. OTMol: Robust Molecular Structure Comparison via Optimal Transport.Journal of chemical information and modeling · 2025
    Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Amit SingerDepartment of Mathematics, Princeton University, Princeton, NJ, USA.
Ruiyi YangProgram in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.ORCID https://orcid.org/0000-0002-0179-6165

Funding

Reconstruction of heterogeneous and small macromolecules by cyro-EMR01GM136780 · NIGMS · PRINCETON UNIVERSITY · PI SINGER, AMIT · 2020 to 2023
$1.3M
NIGMS NIH HHS R01 GM136780
6 · The paper itself

Abstract

In this article, we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Euclidean counterpart and Bayesian optimization is employed for computation. Numerical experiments show improved accuracy and efficiency over existing algorithms on the alignment of real protein molecules. In the context of aligning heterogeneous pairs, we illustrate a potential need for new distance functions.

Indexed as

Bayesian optimizationcryogenic electron microscopyheterogeneity analysisvolume alignmentWasserstein distance

Identifiers

PMID38617997
PMCPMC11016369

What OpenQuestion holds

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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.