Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Sociodemographic disadvantage and low healthcare access as contributing factors to high maternal death in Papua, Indonesia: multilevel analysis of census population data.

Rural and remote health·2026

Same author

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026

Same author

Geospatial disparities in infant mortality in Ghana: evidence from national data.

BMJ global health·2026

Same author

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.

Statistics in biosciences·2026

Same author

Association between household type and false reporting of smoking among South Korean adults.

Tobacco control·2026

Same author

BDDN: bayesian dynamic differential network analysis in cancer proteomics.

BMC bioinformatics·2026

Search research articles

Related Experiment Video

Updated: Feb 17, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

iDINGO-integrative differential network analysis in genomics with Shiny application.

Caleb A Class¹, Min Jin Ha¹, Veerabhadran Baladandayuthapani¹

¹Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Bioinformatics (Oxford, England)

|December 2, 2017

Summary

This study introduces iDINGO, an R package for integrative differential network analysis across multiple omics data types. It accounts for biological hierarchies, improving understanding of disease-related network rewiring.

More Related Videos

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Related Experiment Videos

Last Updated: Feb 17, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Differential network analysis is crucial for understanding disease progression and network rewiring.
Existing tools like DINGO analyze single omics data, neglecting inter-platform hierarchies.
Independent modeling of multiple omics data fails to capture biological data structures.

Purpose of the Study:

To develop an R package, iDINGO, for integrative differential network analysis.
To account for the biological hierarchy among multiple omics data platforms.
To facilitate group-specific dependency estimation and inference on integrative differential networks.

Main Methods:

Developed the iDINGO R package for integrated differential network analysis.
Incorporated biological hierarchy considerations across multiple omics data.
Created a Shiny application for user-friendly analysis and visualization.

Main Results:

iDINGO enables estimation of group-specific dependencies and inference on integrative differential networks.
The tool considers the biological hierarchy among omics data platforms.
Results include visualization of integrative differential networks and identification of cross-platform hub genes.

Conclusions:

iDINGO provides a novel approach for analyzing multi-omics data in differential network analysis.
The package enhances understanding of biological system differences across patient groups.
It offers improved insights into disease mechanisms by integrating hierarchical omics data.