Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.1K
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

7.4K
Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
7.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.

Nature communications·2026
Same author

sPGGM: a sample-perturbed Gaussian graphical model for identifying pre-disease stages and signaling molecules of disease progression.

National science review·2025
Same author

Ultralow-Dimensionality Reduction for Identifying Critical Transitions by Spatial-Temporal PCA.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data.

National science review·2025
Same author

One-core neuron deep learning for time series prediction.

National science review·2025
Same author

DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation.

Chaos (Woodbury, N.Y.)·2024

Related Experiment Video

Updated: Feb 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K

BCTI: a Bayesian network-based method for revealing critical transitions in complex biological systems.

Yuyan Tong1, Renhao Hong1, Na Yang1

  • 1School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China.

Peerj
|February 18, 2026
PubMed
Summary

We developed Bayesian Critical Transitions Inference (BCTI) to detect critical disease states using gene regulatory networks. BCTI identifies early-warning signals for precision medicine and reveals underlying molecular mechanisms.

Keywords:
Bayesian network structure learningCritical transitionDisease progressionDynamic network biomarker (DNB)Gene regulatory network (GRN)

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Related Experiment Videos

Last Updated: Feb 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • Identifying critical states in disease progression is crucial for prevention and precision therapies.
  • Traditional methods for early-warning signals often ignore causal relationships, limiting mechanistic insights.
  • Understanding molecular regulatory mechanisms is key to deciphering disease dynamics.

Purpose of the Study:

  • To develop a novel computational method for detecting critical transitions in biological systems.
  • To integrate network topology dynamics and system state evaluation for robust early-warning signal detection.
  • To enhance the interpretability of molecular regulatory mechanisms driving disease progression.

Main Methods:

  • Bayesian Critical Transitions Inference (BCTI) integrates mutual information and structural equation models.
  • BCTI captures dynamic changes in gene regulatory network topology over time or disease stages.
  • A network scoring mechanism quantitatively evaluates system states for critical transition detection.

Main Results:

  • BCTI demonstrated superior or comparable accuracy to benchmark methods in inferring gene regulatory networks.
  • The method effectively detected critical states in simulated and real biological datasets.
  • BCTI provides new insights into precision medicine and molecular regulation from high-dimensional expression data.

Conclusions:

  • BCTI enables effective detection of critical transitions and dynamic regulatory mechanisms.
  • The method shows strong potential for applications in systems biology and precision medicine.
  • BCTI aids in exploring key molecular drivers of disease progression and development.