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

Probabilistic modeling of systematic errors in two-hybrid experiments.

David Sontag1, Rohit Singh, Bonnie Berger

  • 1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. dsontag@mit.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|November 10, 2007
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

Large language models are powerful electronic health record encoders.

NPJ digital medicine·2026
Same author

Promera: a unified model for biomolecular structure prediction, filtering, and design.

bioRxiv : the preprint server for biology·2026
Same author

Thousandfold Expansion Microscopy.

bioRxiv : the preprint server for biology·2026
Same author

SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins.

ArXiv·2026
Same author

Multi-resolution modeling of a discrete stochastic process identifies causes of cancer.

... International Conference on Learning Representations·2026
Same author

Assessing the effects of fluids and antibiotics in an acute murine model of sepsis: study protocol for the National Preclinical Sepsis Platform-01 (NPSP-01) Study.

F1000Research·2026
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 introduces a new probabilistic method to reduce errors in protein-protein interaction (PPI) studies. The approach improves the accuracy of identifying true interactions, significantly enhancing data reliability in high-throughput experiments.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Two-hybrid (2H) experiments are widely used for high-throughput protein-protein interaction (PPI) network elucidation.
  • These experiments suffer from high error rates, particularly a high false-positive rate.
  • Existing methods often model errors as random and independent, failing to address systematic biases.

Purpose of the Study:

  • To develop a novel probabilistic approach for estimating errors in 2H experiments.
  • To create a comprehensive error model that accounts for both random and systematic errors.
  • To improve the accuracy and reliability of PPI network data derived from 2H assays.

Main Methods:

  • A probabilistic relational model is proposed to explicitly model systematic errors in 2H data.

Related Experiment Videos

  • Markov Chain Monte Carlo (MCMC) algorithms are employed for computation.
  • The model estimates the probability of observed interactions being true and identifies self-activating or promiscuous proteins.

    • Main Results:

    • The novel approach explicitly models systematic errors, unlike previous methods.
    • The proposed method demonstrated a 5-10% overall improvement compared to Bader et al.'s method.
    • In specific scenarios, prediction accuracy was enhanced by up to 76%.

    Conclusions:

    • Explicitly modeling the sources of noise in 2H systems leads to better utilization of experimental data.
    • This probabilistic approach offers a more accurate way to assess confidence in predicted protein-protein interactions.
    • The findings have significant implications for the construction and interpretation of PPI networks.