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

Techniques to cope with missing data in host-pathogen protein interaction prediction.

Meghana Kshirsagar1, Jaime Carbonell, Judith Klein-Seetharaman

  • 1School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)
|September 11, 2012
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

Characterization of Recombinant GMPR from <i>Pocillopora damicornis</i> and Potential Mechanisms of Cold-Induced Metabolic Adaptation.

Biology·2026
Same author

Comprehensive and quantitative molecular docking analysis of rhodopsin-retinal interactions.

Biophysical journal·2026
Same author

Author Correction: 7-Dehydrocholesterol is an endogenous suppressor of ferroptosis.

Nature·2026
Same author

Three Unrelated Children With Childhood Apraxia of Speech: Exome Sequencing and Functional Gene Analysis Imply a Role of Laminin-511 in Early Neurodevelopment.

Case reports in genetics·2026
Same author

Predictive Models for Kidney Offer Acceptance: Challenges and Strategies.

Journal of transplantation·2026
Same author

Allostery-Driven Substrate Gating in the Chlorothalonil Dehalogenase from <i>Pseudomonas</i> sp. CTN-3.

Biology·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

This study introduces advanced missing value imputation techniques to enhance protein-protein interaction (PPI) prediction models. These methods significantly improve accuracy, particularly for host-pathogen interactions, by effectively handling incomplete datasets.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Protein-protein interaction (PPI) prediction commonly uses integrated data features.
  • Missing values in host-pathogen PPI datasets (58-85%) pose significant challenges for machine learning algorithms.

Purpose of the Study:

  • To develop and evaluate specialized missing value imputation techniques for improving PPI prediction.
  • To address the challenge of high missing data rates in host-pathogen interaction datasets.

Main Methods:

  • Utilized cross-species information combined with Group Lasso and ℓ(1)/ℓ(2) regularization for imputation.
  • Applied machine learning techniques to predict protein-protein interactions.

Main Results:

Related Experiment Videos

  • Achieved high prediction accuracy for Salmonella-human PPI prediction (77.6% precision, 84% recall).
  • Demonstrated a 9-point improvement in F1 score over existing methods.
  • Successfully applied the approach to Yersinia-human PPI prediction, showing generalizability.

Conclusions:

  • Specialized missing value imputation significantly enhances PPI prediction model performance.
  • The proposed method is effective and generalizable for host-pathogen PPI prediction.