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

RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

9.6K
Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
9.6K

You might also read

Related Articles

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

Sort by
Same author

Novel Molecules Generation Using Graph Generative Adversarial Networks.

Molecular informatics·2026
Same author

Dynamic Bernstein GCN for Pan-Cancer Subtype Classification Using RNA-Seq and CNV Data.

IEEE transactions on computational biology and bioinformatics·2026
Same author

Exploring coronavirus sequence motifs through convolutional neural network for accurate identification of COVID-19.

Computer methods in biomechanics and biomedical engineering·2024
Same author

Texture based feature extraction using symbol patterns for facial expression recognition.

Cognitive neurodynamics·2024
Same author

Coot-Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences.

Computer methods in biomechanics and biomedical engineering·2023
Same author

ReGen-DTI: A novel generative drug target interaction model for predicting potential drug candidates against SARS-COV2.

Computational biology and chemistry·2023

Related Experiment Video

Updated: Oct 1, 2025

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.3K

A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature

Santhosh Amilpur1, Raju Bhukya1

  • 1Computer Science and Engineering, National Institute of Technology Warangal, Warangal Telangana 506004, India.

Journal of Bioinformatics and Computational Biology
|March 10, 2022
PubMed
Summary

Identifying gene regulatory elements called enhancers is challenging. This study proposes a novel two-layer machine learning model that accurately predicts enhancer locations and strength using advanced feature extraction techniques.

Keywords:
Enhancersartificial neural networkfeature extractiongene regulationgenome annotation

More Related Videos

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines
10:46

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Published on: June 2, 2018

9.5K
AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain
09:59

AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

Published on: March 31, 2022

2.9K

Related Experiment Videos

Last Updated: Oct 1, 2025

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation
09:07

Prediction and Validation of Gene Regulatory Elements Activated During Retinoic Acid Induced Embryonic Stem Cell Differentiation

Published on: June 21, 2016

8.3K
Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines
10:46

Dissection of Enhancer Function Using Multiplex CRISPR-based Enhancer Interference in Cell Lines

Published on: June 2, 2018

9.5K
AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain
09:59

AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain

Published on: March 31, 2022

2.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Enhancers are crucial regulatory DNA elements controlling gene expression.
  • Identifying enhancers and their regulatory strength is difficult due to their dynamic nature.
  • Existing machine learning methods for enhancer identification require improved accuracy and efficiency.

Purpose of the Study:

  • To develop a robust two-layer prediction model for accurate enhancer identification and strength prediction.
  • To enhance feature extraction strategies for improved prediction performance.
  • To address the limitations of current machine learning approaches in enhancer prediction.

Main Methods:

  • Proposed a two-layer prediction model utilizing an artificial neural network (ANN).
  • Implemented an enhanced feature extraction strategy combining Position-Specific Amino Acid Propensity (PSTKNC), Enhanced Nucleic Acid Composition (ENAC), and Composition of k-spaced Nucleic Acid Pairs (CKSNAP).
  • Validated the model on a benchmark chromatin dataset from nine cell lines using 10-fold cross-validation.

Main Results:

  • The proposed model achieved high accuracy (94.50%) and Matthew's Correlation Coefficient (MCC) of 0.8903 in predicting enhancers.
  • The model demonstrated strong performance on an independent test set.
  • The combined feature extraction strategy significantly improved prediction accuracy compared to existing methods.

Conclusions:

  • The developed two-layer prediction model with enhanced feature extraction offers a significant advancement in enhancer identification and strength prediction.
  • The model's high accuracy and MCC indicate its potential for biological applications.
  • Further improvements in machine learning algorithms can enhance the efficiency and accuracy of predicting gene regulatory elements.