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

Linking gene expression and functional network data in human heart failure.

Anyela Camargo1, Francisco Azuaje

  • 1School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, Northern Ireland, United Kingdom.

Plos One
|December 21, 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

Where Medical Statistics Meets Artificial Intelligence.

The New England journal of medicine·2023
Same author

Correction: Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study.

BMC medical research methodology·2023
Same author

Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study.

BMC medical research methodology·2023
Same author

Allergic airway inflammation delays glioblastoma progression and reinvigorates systemic and local immunity in mice.

Allergy·2022
Same author

DrDimont: explainable drug response prediction from differential analysis of multi-omics networks.

Bioinformatics (Oxford, England)·2022
Same author

Oncolytic H-1 parvovirus binds to sialic acid on laminins for cell attachment and entry.

Nature communications·2021
Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026
Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026
Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026
Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026
Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026
Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026
See all related articles

Integrating gene expression and protein-protein interaction (PPI) networks in heart failure (HF) reveals key insights. This approach aids in identifying potential biomarkers and drug targets for HF by analyzing network connectivity and gene expression patterns.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Gene expression profiling and protein-protein interaction (PPI) network analysis are crucial for identifying disease biomarkers and drug targets.
  • An integrative approach combining these data sources is essential for advancing systems medicine.
  • This study focuses on human heart failure (HF) as a model pathological condition.

Purpose of the Study:

  • To perform an integrative bioinformatics analysis of gene expression and PPI networks in human heart failure.
  • To assemble a global PPI network relevant to HF and analyze its interaction patterns in conjunction with gene expression data.
  • To explore the relationships between gene expression significance, PPI network connectivity, and co-expression patterns.

Main Methods:

Related Experiment Videos

  • Construction of a global protein-protein interaction (PPI) network for human heart failure (HF).
  • Analysis of gene expression data in relation to the assembled HF PPI network.
  • Investigation of correlations between gene co-expression and PPI network connectivity.
  • Gene Ontology (GO) analysis of highly-connected versus low-connectivity proteins.

    • Main Results:

    • Established relationships between differential gene expression significance and PPI network connectivity degrees.
    • Found that highly-connected proteins are not always encoded by significantly differentially expressed genes.
    • Observed diverse co-expression patterns for genes encoding network hubs and superhubs compared to peripheral nodes.
    • Gene Ontology analysis indicated that highly-connected proteins are associated with higher-level biological processes, while low-connectivity proteins relate to specific disease processes.

    Conclusions:

    • The integrative analysis of differential gene expression and PPI networks enhances understanding of functional roles in heart failure.
    • This approach facilitates the identification of potential therapeutic targets for human heart failure.
    • Supports the utility of systems biology approaches in uncovering disease mechanisms and therapeutic strategies.