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

Diffusion01:12

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

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

Sort by
Same author

HiC4D-SPOT: a spatiotemporal outlier detection tool for Hi-C data.

Briefings in bioinformatics·2025
Same author

scHiGex: predicting single-cell gene expression based on single-cell Hi-C data.

NAR genomics and bioinformatics·2025
Same author

PANDA-3D: protein function prediction based on AlphaFold models.

NAR genomics and bioinformatics·2024
Same author

C2c: Predicting Micro-C from Hi-C.

Genes·2024
Same author

EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks.

International journal of molecular sciences·2024
Same author

DeepChIA-PET: Accurately predicting ChIA-PET from Hi-C and ChIP-seq with deep dilated networks.

PLoS computational biology·2023
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: May 12, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

8.7K

Learning Micro-C from Hi-C with diffusion models.

Tong Liu1, Hao Zhu1, Zheng Wang1

  • 1Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America.

Plos Computational Biology
|May 17, 2024
PubMed
Summary
This summary is machine-generated.

HiC2MicroC uses denoising diffusion probabilistic models to predict Micro-C chromatin interactions from Hi-C data, enhancing loop detection and genomic feature analysis. This method improves upon existing regression techniques, offering a valuable tool for analyzing chromatin organization.

More Related Videos

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
00:10

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.2K
Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.1K

Related Experiment Videos

Last Updated: May 12, 2026

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

8.7K
Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
00:10

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.2K
Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.1K

Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Micro-C offers nucleosome resolution chromatin interaction data, surpassing traditional Hi-C in signal-to-noise and loop detection.
  • Limited Micro-C datasets hinder comprehensive analysis compared to abundant Hi-C data.

Purpose of the Study:

  • To develop a computational method (HiC2MicroC) for predicting Micro-C data from existing Hi-C datasets.
  • To leverage denoising diffusion probabilistic models (DDPM) for enhanced chromatin interaction prediction.

Main Methods:

  • Trained DDPM and regression models using human foreskin fibroblast (HFFc6) cell line data.
  • Evaluated prediction accuracy across six cell types at 5-kb and 1-kb resolutions.
  • Compared HiC2MicroC performance against regression models and validated against P পাঁচটিCMicro-C and ChIA-PET data.

Main Results:

  • HiC2MicroC successfully recovered Micro-C loops, including those missed by Hi-C.
  • Predicted loops frequently anchored CTCF binding sites convergently.
  • Recovered loops exhibited genomic and epigenetic properties consistent with Micro-C data, linking enhancers and promoters.

Conclusions:

  • HiC2MicroC effectively enhances Hi-C data towards Micro-C resolution using DDPM.
  • The method accurately predicts biologically relevant chromatin loops, validated by multiple experimental techniques.
  • HiC2MicroC provides a powerful computational tool for in-depth analysis of chromatin organization from Hi-C data.