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

Multiple Regression01:25

Multiple Regression

4.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.3K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.1K
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
1.1K
Conserved Binding Sites01:49

Conserved Binding Sites

5.3K
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...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence.

Nature communications·2025
Same author

Tutorial: guidelines for the use of machine learning methods to mine genomes and proteomes for antibiotic discovery.

Nature protocols·2025
Same author

Leveraging large language models for peptide antibiotic design.

Cell reports. Physical science·2025
Same author

Improving functional protein generation via foundation model-derived latent space likelihood optimization.

bioRxiv : the preprint server for biology·2025
Same author

Venomics AI: a computational exploration of global venoms for antibiotic discovery.

bioRxiv : the preprint server for biology·2025
Same author

Exploration of DPP-IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline That Identifies the Restriction Enzyme Cutting Site.

ACS omega·2023
Same journal

Experimental study on deantigenization and trabecular structure effects on bovine cancellous bone compression.

Bio-medical materials and engineering·2026
Same journal

Effects of dentin extract without demineralization on migration and angiogenic potential of human umbilical vein endothelial cells.

Bio-medical materials and engineering·2026
Same journal

Measurement of thermal expansion coefficient of melanin for photoacoustic technology.

Bio-medical materials and engineering·2026
Same journal

Development of chitosan-selenium nanoparticle modified brushite cement: A potential strategy for improved clinical performance in bone regeneration.

Bio-medical materials and engineering·2026
Same journal

Electrostatic layer-by-layer assembly for fabricating morphology-controlled hydroxyapatite/zirconia composite with enhanced osteogenic performance.

Bio-medical materials and engineering·2026
Same journal

The antitumor activity of bismuth lipophilic nanoparticles (BisBAL NPs) on human glioblastoma is higher than temozolomide.

Bio-medical materials and engineering·2026
See all related articles

Related Experiment Video

Updated: Apr 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Predicting a DNA-binding protein using random forest with multiple mathematical features.

Changge Guan1, Xiaohui Niu2, Feng Shi3

  • 1College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070, P.R. of China.

Bio-Medical Materials and Engineering
|September 26, 2015
PubMed
Summary
This summary is machine-generated.

Identifying DNA-binding proteins is crucial for biology. This study uses mathematical features like fractal dimension and information entropy with random forest to accurately predict DNA-binding proteins, achieving 81.57% accuracy.

Keywords:
DNA-binding proteinsHilbert-Huang transformationfractal dimensioninformation entropyrandom forest

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

Related Experiment Videos

Last Updated: Apr 3, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • DNA-binding proteins are essential for numerous biological processes.
  • Accurate identification of DNA-binding proteins remains a significant challenge in molecular biology.

Purpose of the Study:

  • To develop a robust prediction model for identifying DNA-binding proteins.
  • To explore the utility of various mathematical features for predicting DNA-binding proteins.

Main Methods:

  • A random forest model was constructed using nine classes of candidate features derived from mathematical fields.
  • Features included fractal dimension, conjoint triad feature, Hilbert-Huang Transformation, amino acid composition, dipeptide composition, chaos game representation, and information entropies.
  • Model performance was evaluated using a 5-fold cross-validation test on the DNA-Prot dataset.

Main Results:

  • The combination of amino acid composition, fractal dimension, and information entropies of amino acid and chaos game representation yielded the best prediction performance.
  • The model achieved an accuracy of 0.8157 and a Matthew's correlation coefficient (MCC) of 0.5968.
  • Fractal dimension and information entropy were identified as vital features providing complementary sequence-order information.

Conclusions:

  • Mathematical features, particularly fractal dimension and information entropy, are effective for predicting DNA-binding proteins.
  • These features offer valuable sequence-order insights beyond basic amino acid composition.
  • The developed model demonstrates significant potential for identifying DNA-binding proteins in biological research.