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

Node-based differential network analysis in genomics.

Xiao-Fei Zhang1, Le Ou-Yang2, Hong Yan3

  • 1School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China; Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.

Computational Biology and Chemistry
|April 9, 2017
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

A highly sensitive and specific electrochemical sensing method for robust detection of Escherichia coli lac Z gene sequence.

Biosensors & bioelectronics·2015
Same author

Stereoselective intermolecular nitroaminoxylation of terminal aromatic alkynes: trapping alkenyl radicals by TEMPO.

Organic letters·2014
Same author

Correlation between red blood cell transfusion volume and mortality in patients with massive blood transfusion: A large multicenter retrospective study.

Experimental and therapeutic medicine·2014
Same author

[Sampling survey of schistosomiasis prevention knowledge among middle school students in endemic areas of Hubei Province].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2014
Same author

Synthesis of ultrahighly electron-deficient pyrrolo[3,4-d]pyridazine-5,7-dione by inverse electron demand Diels-Alder reaction and its application as electrochromic materials.

Organic letters·2014
Same author

Quantitative proteomics analysis by iTRAQ in human nuclear cataracts of different ages and normal lens nuclei.

Proteomics. Clinical applications·2014
Same journal

Protein dynamic simulations: From early inception to clinical translation over half a century.

Computational biology and chemistry·2026
Same journal

Integrated omics and virtual screening predict Tabularin as a dual inhibitor of the prognostic microRNAs mir-19a and mir-32 in colorectal cancer.

Computational biology and chemistry·2026
Same journal

In silico characterization of acetyl-CoA carboxylase from Staphylococcus aureus and Escherichia coli: A comparative analysis.

Computational biology and chemistry·2026
Same journal

An optimized cascaded transformer with progressive attention for lung and colon cancer diagnosis from histopathological images.

Computational biology and chemistry·2026
Same journal

From cross cancer transcriptomics to therapeutics: WGX-50 target hub genes in breast cancer and non-small cell lung carcinoma.

Computational biology and chemistry·2026
Same journal

Blood-based biomarker discovery through integrative transcriptomic and miRNA network analyses in schizophrenia, major depressive disorder, and bipolar disorder.

Computational biology and chemistry·2026
See all related articles

Gene dependency networks change with conditions. This study introduces a new node-based differential network analysis (N-DNA) model to better identify key genes driving these network changes, outperforming previous methods.

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene dependency networks dynamically change under different conditions.
  • Existing differential network analysis methods often focus on individual edge changes, potentially missing crucial hub genes.
  • Identifying key players driving network alterations is vital for understanding biological processes and disease mechanisms.

Purpose of the Study:

  • To develop a novel node-based differential network analysis (N-DNA) model.
  • To directly estimate differential networks driven by hub nodes, rather than just edge changes.
  • To improve the accuracy of differential network inference.

Main Methods:

  • Modeled condition-specific gene networks as precision matrices.
  • Defined the differential network as the difference between two precision matrices.
Keywords:
Differential network analysisGaussian graphical modelGene dependency networkGraphical lassoHub nodes

Related Experiment Videos

  • Formulated a convex optimization problem using a D-trace loss and a row-column overlap norm penalty.

    • Main Results:

    • N-DNA demonstrated more accurate differential network estimation compared to existing approaches in simulations.
    • Application to ovarian and breast cancer data successfully identified known cancer-related genes.
    • The model generated novel predictions for further investigation.

    Conclusions:

    • N-DNA offers a more effective approach for identifying key genes that drive differential network structures.
    • This node-centric method enhances the understanding of condition-specific gene network alterations.
    • The findings have implications for cancer research and biomarker discovery.