Evidence map›Paper›PMID 38233621›Full record

ArticleNature2024

Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly.

Constantine Glen Evans, Jackson O'Brien, Erik Winfree, Arvind Murugan

Abstract read
In one paragraph

Article in Nature, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Combinatorial decision-making driven by multicomponent surface condensates.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  6. Article
  7. Article
  8. Dynamic Decoration of DNA Scaffolds for High-Resolution Cancer Cell Subtyping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  9. Diverse, distinct, and densely packed DNA nanostar droplets.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  10. Theoretical limits for sensing through phase separation.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  11. Article
  12. Article
  13. Trainable computation in molecular networks.bioRxiv : the preprint server for biology · 2025
    Article
  14. Article
  15. Hierarchical self-assembly for high-yield addressable complexity at fixed conditions.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

Constantine Glen EvansCalifornia Institute of Technology, Pasadena, CA, USA. cge@dna.caltech.edu.ORCID 0000-0002-7053-1670
Jackson O'BrienUniversity of Chicago, Chicago, IL, USA. jdobrien07@gmail.com.ORCID 0000-0002-5221-2107
Erik WinfreeCalifornia Institute of Technology, Pasadena, CA, USA. winfree@caltech.edu.ORCID 0000-0002-5899-7523
Arvind MuruganUniversity of Chicago, Chicago, IL, USA. amurugan@uchicago.edu.ORCID 0000-0001-5464-917X

Funding

Revealing mechanisms of specificity and adaptability in molecular information processing through data-driven modelsR35GM151211 · NIGMS · UNIVERSITY OF CHICAGO · PI Arvind Murugan · 2023 to 2026
$1.5M
NIGMS NIH HHS R35 GM151211
6 · The paper itself

Abstract

Inspired by biology's most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles

Indexed as

DNANeural Networks, ComputerPattern Recognition, AutomatedComputer SimulationKineticsMicroscopy, Atomic ForceMicroscopy, FluorescenceDNA

Identifiers

PMID38233621
PMCPMC10794147

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.