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 Experiment Videos

Using Chou's 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature.

Xiuquan Du1,2, Jiajia Hu2, Shuo Li3

  • 1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, Anhui, China.

Journal of Proteome Research
|February 1, 2021
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Effects of Lactarius hatsudake Fortification on Physicochemical, Microbiological and Antioxidant Properties of Stirred-Type Yogurt during Cold Storage.

Food science of animal resources·2026
Same author

A High-Affinity Antibody for Rapid Screening of PFAS: Breaking Immunological Inertness through Descriptor-Guided Hapten Design.

Analytical chemistry·2026
Same author

TetR-based biosensors for tetracycline detection: A review of allosteric mechanism, applications, and engineering.

Talanta·2026
Same author

An optimized engineered bacterium for tyrosinemia type 1 therapy: A multi-species preclinical study.

Molecular therapy : the journal of the American Society of Gene Therapy·2026
Same author

Diagnostic Performance of <sup>68</sup>Ga-FAPI PET/CT Versus <sup>18</sup>F-FDG PET/CT in Evaluating Neoadjuvant Therapy Pathological Response in Breast Cancer.

NPJ breast cancer·2026
Same author

A Single-Cell Proteomic Atlas of Cardiac Fibrosis Reveals Cell-Type-Specific Remodeling and Discordant Protein Regulation.

Proteomics·2026
Same journal

From Method-Defined Signals to Reference Measurement Procedures: Two Decades of Mass Spectrometry-Based ProGRP Quantification.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Extracellular Vesicle-Enriched Plasma Using Mag-Net for Biomarker Discovery in Pancreatic Ductal Adenocarcinoma.

Journal of proteome research·2026
Same journal

Computationally Efficient Bayesian Estimation of Graphical Networks for Omics Data.

Journal of proteome research·2026
Same journal

Hierarchy of MS-Based Evidence.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Exosomes from HPV-Positive and HPV-Negative Head and Neck Squamous Cell Carcinoma: Selective Cargo Packaging.

Journal of proteome research·2026
Same journal

Proteomic Analysis Identifies ATE1-Dependent Arginylation Dysregulation across Meningioma Grades.

Journal of proteome research·2026
See all related articles

This study introduces a novel multi-scale CNN approach for DNA-protein binding (DPB) prediction. The method fuses diverse sequence features, significantly improving prediction accuracy over existing techniques.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • DNA-protein binding (DPB) is crucial for gene expression regulation.
  • Traditional DPB prediction methods struggle with manually extracted features.
  • Deep learning, particularly CNNs, has improved DPB prediction but often ignores feature complementarity.

Purpose of the Study:

  • To develop an advanced method for accurate DNA-protein binding prediction.
  • To overcome limitations of existing methods by integrating multiple sequence features.
  • To enhance understanding of gene expression regulation through improved DPB prediction.

Main Methods:

  • A novel multi-scale Convolutional Neural Network (CNN) model was developed.
  • Distinct DNA sequence features were generated using sliding windows of varying lengths.
Keywords:
DNA-protein bindingfeature fusionmulti-scale complementary feature

Related Experiment Videos

  • Multiple feature sequences were fused and encoded for comprehensive representation.
  • The fused features were analyzed using multi-scale CNNs to capture complex dependencies and complementary information.

    • Main Results:

    • The proposed method achieved an average AUC of 0.9112 across 690 ChIP-seq datasets.
    • This performance significantly surpasses the latest existing DPB prediction methods.
    • The model effectively learns from fused sequence features, leveraging complementary CNN-extracted attributes.

    Conclusions:

    • The developed multi-scale CNN method is highly effective for DNA-protein binding prediction.
    • Feature fusion and multi-scale analysis enhance the accuracy and robustness of DPB prediction.
    • The method offers a valuable tool for computational biology research and understanding gene regulation.