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

Susceptibility, Permittivity and Dielectric Constant01:26

Susceptibility, Permittivity and Dielectric Constant

3.2K
When placed in an external electric field, a dielectric material gets polarized. The charge density in the dielectric material is given by the sum of the bound and free charge densities, while the total charge density can also be written in terms of the total electric field. The bound charge density can be measured in terms of polarization, leading to the relationship between electric displacement and polarization.
3.2K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.3K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.3K
Magnetic Susceptibility and Permeability01:31

Magnetic Susceptibility and Permeability

2.5K
In linear magnetic materials, like paramagnets and diamagnets, magnetization is proportional to the magnetic field intensity. The constant of proportionality, a dimensionless number, is called magnetic susceptibility. The value of the susceptibility depends on the type of material.
When diamagnetic materials are placed under an external magnetic field, the moments opposite to the field are induced. Hence, the susceptibility for diamagnets has a minimal negative value of 10-5–10-6. Since...
2.5K
2D NMR: Overview of Homonuclear Correlation Techniques01:16

2D NMR: Overview of Homonuclear Correlation Techniques

716
Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
COSY90 is the standard two-dimensional (2D) COSY experiment that...
716
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

838
Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
838
Correlation and Regression00:53

Correlation and Regression

3.7K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Fully Synthetic, Biomimicking Polysulfates With Tunable Anticoagulant and Endothelial Cell-Selective Bioactivity.

Macromolecular bioscience·2026
Same author

Human microglial transitions at the Aβ-tau inflection point associate with divergent pathways to dementia and resilience.

Nature medicine·2026
Same author

Lost in Retraining: Closed-Loop Learning and Model Collapse in Exponential Families.

Physical review letters·2026
Same author

Multi-Network Co-expression Analysis Enhances Biological Insights from Single-Cell Gene Expression.

Interdisciplinary sciences, computational life sciences·2026
Same author

Cross-feeding percolation phase transitions of intercellular metabolic networks.

Science advances·2025
Same author

Bayesian inference of minimally complex models with interactions of arbitrary order.

Physical review. E·2025
Same journal

Kinesin-5/Cut7 C-terminal tail phosphorylation influence on motor regulation through multi-scale molecular modeling.

Biophysical journal·2026
Same journal

Dynamic conformations of fluorophores on self-labeling protein tags.

Biophysical journal·2026
Same journal

Different actions of RyR2 open and closed channel block explained by a multiscale Ca<sup>2+</sup> release model.

Biophysical journal·2026
Same journal

Membrane Environment Sets the Functional pK<sub>a</sub> of Ionizable Lipids.

Biophysical journal·2026
Same journal

Distinguishable spreading dynamics in microbial communities.

Biophysical journal·2026
Same journal

Phylogeny of SK channels and functional characterization of the conserved Phe in the S3-S4 loop.

Biophysical journal·2026
See all related articles

Related Experiment Video

Updated: Feb 26, 2026

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts
08:43

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

Published on: December 1, 2018

12.1K

Translating ceRNA Susceptibilities into Correlation Functions.

Araks Martirosyan1, Matteo Marsili2, Andrea De Martino3

  • 1Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy; VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium.

Biophysical Journal
|July 13, 2017
PubMed
Summary
This summary is machine-generated.

We found that correlation functions can measure the strength of competing endogenous RNA (ceRNA) interactions. This provides a simpler way to analyze ceRNA networks and transcriptional data.

More Related Videos

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells
14:12

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

6.1K
Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

7.6K

Related Experiment Videos

Last Updated: Feb 26, 2026

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts
08:43

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

Published on: December 1, 2018

12.1K
Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells
14:12

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

6.1K
Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

7.6K

Area of Science:

  • Biochemistry
  • Genomics
  • Bioinformatics

Background:

  • MicroRNA (miRNA) binding competition creates crosstalk between target molecules, termed competing endogenous RNAs (ceRNAs).
  • Quantifying ceRNA crosstalk strength under physiological conditions is challenging, limiting understanding of its biological significance.

Purpose of the Study:

  • To develop a method for quantifying ceRNA crosstalk susceptibility using measurable quantities.
  • To provide new tools for analyzing transcriptional data and probing ceRNA networks computationally.

Main Methods:

  • Utilized correlation functions to encode the susceptibility of ceRNAs to perturbations affecting their competitors.
  • Performed extensive numerical simulations to validate the proposed method.
  • Re-analyzed the crosstalk pattern of phosphatase and tensin homolog (PTEN) from The Cancer Genome Atlas (TCGA) breast cancer database.

Main Results:

  • Correlation functions effectively capture the tendency of ceRNAs to crosstalk.
  • The method provides a simplified and intuitive approach to estimate ceRNA crosstalk intensity.
  • Validated findings through numerical simulations and real-world cancer genomic data.

Conclusions:

  • Correlation functions offer a novel and accessible metric for assessing ceRNA crosstalk.
  • This approach simplifies the analysis of complex ceRNA networks.
  • Provides new computational tools for investigating gene regulatory networks in silico.