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

Epigenetic Regulation01:37

Epigenetic Regulation

3.1K
Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
3.1K
Replicative Cell Senescence02:15

Replicative Cell Senescence

3.7K
Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds...
3.7K
Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

11.9K
As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
11.9K

You might also read

Related Articles

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

Sort by
Same author

EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.

Briefings in bioinformatics·2026
Same author

FL-SDGIN: A federated graph learning approach for schizophrenia diagnosis integrating static and dynamic brain functional networks.

Schizophrenia research·2026
Same author

Identification and Prognostic Analysis of Immune-Related Genes Co-Regulated by Key Histone Modifications in Breast Cancer.

Current issues in molecular biology·2026
Same author

Identification of ERN1 as a Potential Context-Dependent Biomarker in Chronic Obstructive Pulmonary Disease Based on Bioinformatics Analysis of GSE57148 Dataset.

International journal of chronic obstructive pulmonary disease·2026
Same author

Prediction of Digestible and Metabolizable Energy in Swine Feed Using Machine Learning.

ACS omega·2026
Same author

A mechanosensitive lipolytic factor in the bone marrow promotes osteogenesis and lymphopoiesis.

Cell metabolism·2026

Related Experiment Video

Updated: Sep 10, 2025

Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization
17:14

Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization

Published on: December 10, 2012

14.1K

RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA

Fuying Dao1,2, Benjamin Lebeau2, Crystal Chia Yin Ling2

  • 1Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China.

Imeta
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

RepliChrom, a machine learning model, predicts gene regulatory interactions using DNA replication timing. It uncovers cancer-specific chromatin patterns in leukemia, revealing insights into gene regulation in normal and diseased genomes.

More Related Videos

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement
08:06

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Published on: January 19, 2017

8.6K
Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
11:36

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

2.4K

Related Experiment Videos

Last Updated: Sep 10, 2025

Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization
17:14

Chromosome Replicating Timing Combined with Fluorescent In situ Hybridization

Published on: December 10, 2012

14.1K
Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement
08:06

Genome-wide Determination of Mammalian Replication Timing by DNA Content Measurement

Published on: January 19, 2017

8.6K
Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
11:36

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

2.4K

Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Enhancer-promoter interactions are crucial for gene regulation.
  • DNA replication timing influences genome organization and gene expression.
  • Predicting these interactions is challenging due to complex regulatory networks.

Discussion:

  • RepliChrom integrates DNA replication timing with chromatin interaction data.
  • The model accurately identifies true enhancer-promoter interactions.
  • Promoter-region signals are identified as key drivers of gene regulation.

Key Insights:

  • RepliChrom provides an interpretable framework for predicting enhancer-promoter interactions.
  • The model reveals cancer-specific chromatin alterations in leukemia.
  • Mechanistic insights into replication timing's role in gene regulation are uncovered.

Outlook:

  • Potential for broader applications in understanding gene regulation across various cell types and diseases.
  • Further refinement of the model could enhance predictive accuracy.
  • Experimental validation of identified cancer-specific patterns is warranted.