Jove
Visualize
Contact Us

Related Concept Videos

Transcription Factors02:16

Transcription Factors

82.8K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
82.8K
Transcription Elongation Factors02:35

Transcription Elongation Factors

14.0K
Transcription elongation is a dynamic process that alters depending upon the sequence heterogeneity of the DNA being transcribed. Hence, it is not surprising that the elongation complex's composition also varies along the way while transcribing a gene.
The transcription elongation is regulated via pausing of RNA polymerase on several occasions during transcription. In bacteria, these halts are necessary because the transcription of DNA into mRNA is coupled to the translation of that mRNA...
14.0K
Transcription Elongation Factors02:35

Transcription Elongation Factors

4.8K
4.8K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

11.9K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
11.9K
General Transcription Factors01:30

General Transcription Factors

7.1K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
7.1K
Master Transcription Regulators02:23

Master Transcription Regulators

7.8K
Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Drug repositioning by belief networks and ensemble method.

Biomedical physics & engineering express·2026
Same author

Bayesian Inference for Drug Discovery by High Negative Samples and Oversampling.

Bioinformatics and biology insights·2025
Same author

Predicting TF-Target Gene Association Using a Heterogeneous Network and Enhanced Negative Sampling.

Bioinformatics and biology insights·2025
Same author

Label Transfer for Drug Disease Association in Three Meta-Paths.

Evolutionary bioinformatics online·2024
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 Experiment Video

Updated: Feb 7, 2026

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow
07:48

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow

Published on: April 1, 2022

4.7K

Enhancing transcription factor regulatory network analysis through data balancing and representation learning.

Xuan Tho Dang1

  • 1Academy of Policy and Development, Hanoi City, Vietnam.

Biomedical Physics & Engineering Express
|February 5, 2026
PubMed
Summary

This study introduces a novel computational method to accurately predict transcription factor (TF) and target gene interactions, overcoming data imbalance issues for improved cancer research and drug discovery.

Keywords:
K-meansK-means++TF regulatory interactionsdata imbalance handlingheterogeneous biological networksmeta-path

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
11:25

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Published on: February 11, 2019

8.5K

Related Experiment Videos

Last Updated: Feb 7, 2026

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow
07:48

Genome-wide Profiling of Transcription Factor-DNA Binding Interactions in Candida albicans: A Comprehensive CUT&RUN Method and Data Analysis Workflow

Published on: April 1, 2022

4.7K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
11:25

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

Published on: February 11, 2019

8.5K

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Transcription factor (TF) and target gene interactions are crucial for gene regulation and are implicated in diseases like cancer.
  • Experimental methods for TF-target prediction are costly and lack scalability.
  • Data imbalance in machine learning models hinders accurate prediction of TF-target interactions.

Purpose of the Study:

  • To develop a novel computational framework for enhanced prediction of transcription factor-target gene interactions.
  • To address the challenge of data imbalance in TF-target prediction models.
  • To improve the accuracy and generalizability of TF-target interaction prediction for biomedical applications.

Main Methods:

  • Integration of K-means++ clustering with a data balancing strategy to mitigate bias towards low-frequency TFs.
  • Application of deep learning techniques, including random walk sampling and skip-gram embeddings, for biological network representation.
  • Utilizing a five-fold cross-validation approach to evaluate model performance.

Main Results:

  • The proposed method significantly enhances the accuracy of TF-target interaction prediction.
  • Achieved a superior average Area Under the Curve (AUC) of 0.9452 ± 0.0047.
  • Demonstrated improved model generalization by effectively addressing data imbalance.

Conclusions:

  • The novel framework offers a robust solution for accurate TF-target interaction prediction, overcoming data imbalance challenges.
  • This approach has significant implications for advancing molecular biology and biomedical research.
  • Facilitates TF-target gene discovery and aids in the development of novel therapeutic strategies.