Evidence map›Paper›PMID 41675964›Full record

ArticleQuantitative biology (Beijing, China)2025

Action functional as an early warning indicator in the space of probability measures via Schrödinger bridge.

Peng Zhang, Ting Gao, Jin Guo, Jinqiao Duan

Abstract read
In one paragraph

Article in Quantitative biology (Beijing, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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.

Peng ZhangSchool of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China.
Ting GaoSchool of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China.ORCID https://orcid.org/0000-0001-7840-7060
Jin GuoSchool of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China.
Jinqiao DuanGuangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems Great Bay University Dongguan China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Critical transitions and tipping phenomena between two meta-stable states in stochastic dynamical systems are a scientific issue. In this work, we expand the methodology of identifying the most probable transition pathway between two meta-stable states with Onsager-Machlup action functional, to investigate the evolutionary transition dynamics between two meta-stable invariant sets with Schrödinger bridge. In contrast to existing methodologies such as statistical analysis, bifurcation theory, information theory, statistical physics, topology, and graph theory for early warning indicators, we introduce a novel framework on Early Warning Signals (EWS) within the realm of probability measures that align with the entropy production rate. To validate our framework, we apply it to the Morris-Lecar model and investigate the transition dynamics between a meta-stable state and a stable invariant set (the limit cycle or homoclinic orbit) under various conditions. Additionally, we analyze real Alzheimer's data from the Alzheimer's Disease Neuroimaging Initiative database to explore EWS indicating the transition from healthy to pre-AD states. This framework not only expands the transition pathway to encompass measures between two specified densities on invariant sets, but also demonstrates the potential of our early warning indicators for complex diseases.

Indexed as

action functionalAlzheimer’s diseaseearly warning indicatorSchrödinger bridgetransition dynamics

Identifiers

PMID41675964
PMCPMC12806042

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.