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

SAMIE: statistical algorithm for modeling interaction energies.

P V Benos1, A S Lapedes, D S Fields

  • 1Dept. of Genetics, Campus Box 8232, Medical School, Washington University, 4566 Scott Ave., St. Louis, MO 63110, USA. benos@genetics.wustl.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|March 27, 2001
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

Downregulation of estrogen receptor and modulation of growth of breast cancer cell lines mediated by paracrine stromal cell signals.

Breast cancer research and treatment·2016
Same author

Antigenic and genetic evolution of swine influenza A (H3N2) viruses in Europe.

Journal of virology·2007
Same author

Genomic analysis of immediate/early response to shear stress in human coronary artery endothelial cells.

Physiological genomics·2002
Same author

Superadditive correlation.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics·2002
Same author

Genetic correlations in mutation processes.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics·2002
Same author

Identifying muscle regulatory elements and genes in the nematode Caenorhabditis elegans.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2002
Same journal

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
See all related articles

This study models protein-DNA interactions using a data-driven approach with neural networks. The method accurately identifies protein binding sites, matching or exceeding existing techniques.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Mechanics

Background:

  • Understanding protein-DNA interactions is crucial for gene regulation.
  • Existing models may lack quantitative detail or flexibility.
  • Neural network approaches offer novel ways to model complex biological systems.

Purpose of the Study:

  • To develop and validate a data-driven probabilistic model for protein-DNA interactions.
  • To apply a statistical mechanics-based formalism, related to Boltzmann Machines, for this modeling.
  • To assess the model's performance against existing methods.

Main Methods:

  • Utilized SELEX (Systematic Evolution of Ligands by Exponential Enrichment) data for training.
  • Employed a "one-to-one" interaction model where one amino acid contacts one DNA base.

Related Experiment Videos

  • Applied probabilistic algorithms and a neural network-based approach.

    • Main Results:

    • The trained network successfully identified wild-type binding sites for EGR and MIG protein families.
    • Model predictions demonstrated performance comparable to or better than existing literature methods.
    • The "one-to-one" model provided a validated framework for interaction analysis.

    Conclusions:

    • The data-driven, neural network approach provides a powerful tool for modeling protein-DNA interactions.
    • This methodology offers quantitative insights and potential for exploring more complex interaction models.
    • The approach shows promise for advancing our understanding of gene regulation and protein binding.