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

Conserved Binding Sites01:49

Conserved Binding Sites

5.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
5.2K
Conserved Binding Sites01:49

Conserved Binding Sites

2.0K
2.0K
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
Ligand Binding Sites02:40

Ligand Binding Sites

15.1K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
15.1K
Ligand Binding Sites02:40

Ligand Binding Sites

8.8K
8.8K
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

You might also read

Related Articles

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

Sort by
Same author

Single-cell profiling reveals a novel CAF subpopulation linking stromal heterogeneity to immune suppression in breast cancer subtypes.

Journal of translational medicine·2026
Same author

TSProm: deep learning framework to predict tissue-specific regulatory logic.

NAR genomics and bioinformatics·2026
Same author

Development and Validation of a Multivariable Machine Learning Model for Mortality Prediction among Intensive Care Unit Patients.

Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine·2026
Same author

HViLM: A Foundation Model for Viral Genomics Enables Multi-Task Prediction of Pathogenicity, Transmissibility, and Host Tropism.

bioRxiv : the preprint server for biology·2026
Same author

TSProm: Deciphering the Genomic Context of Tissue Specificity.

bioRxiv : the preprint server for biology·2025
Same author

Unveiling Epigenetic Regulatory Elements Associated with Breast Cancer Development.

International journal of molecular sciences·2025
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

A DNABERT based deep learning framework for predicting transcription factor binding sites.

Pratik Dutta1, Nimisha Ghosh2, Daniele Santoni3

  • 1Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India. pratikdutta@soa.ac.in.

Scientific Reports
|February 3, 2026
PubMed
Summary
This summary is machine-generated.

We developed TFBS-Finder, a deep learning model for predicting Transcription Factor Binding Sites (TFBSs). Our model outperforms existing methods, enhancing understanding of gene regulation.

Keywords:
DNA SequencesDNABERTDeep LearningTranscription Factor Binding Sites

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
12:29

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Published on: April 16, 2018

9.7K

Related Experiment Videos

Last Updated: Feb 5, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
12:29

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Published on: April 16, 2018

9.7K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences called Transcription Factor Binding Sites (TFBSs).
  • Accurate TFBS prediction is crucial for deciphering gene regulatory networks and cellular functions.
  • Existing deep learning models for TFBS prediction offer room for improvement.

Purpose of the Study:

  • To develop an advanced deep learning model, TFBS-Finder, for accurate prediction of Transcription Factor Binding Sites.
  • To leverage pre-trained models and novel attention mechanisms for enhanced feature extraction from DNA sequences.

Main Methods:

  • The TFBS-Finder model integrates pre-trained DNABERT for sequence embedding and capturing long-range dependencies.
  • It incorporates Convolutional Neural Network (CNN), Modified Convolutional Block Attention Module (MCBAM), and Multi-Scale Convolutions with Attention (MSCA) for local feature extraction.
  • The model was trained and validated on 165 ENCODE ChIP-seq datasets.

Main Results:

  • TFBS-Finder demonstrated superior performance in TFBS prediction compared to existing methodologies.
  • Ablation studies and cross-cell line validations confirmed the model's effectiveness.
  • Visual analysis provided insights into the interpretability of the prediction results.

Conclusions:

  • The proposed TFBS-Finder model offers a significant advancement in predicting Transcription Factor Binding Sites.
  • Its architecture effectively combines long-range and local feature extraction for improved accuracy.
  • The model's interpretability and superior performance make it a valuable tool for genomic research.