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 Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.5K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
16.5K
Multiple Regression01:25

Multiple Regression

4.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.3K
Regression Analysis01:11

Regression Analysis

8.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.8K
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Genomics02:02

Genomics

41.5K
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...
41.5K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.2K
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Histone deacetylase enzyme activity is not the universal anticancer target of HDAC inhibitors.

Signal transduction and targeted therapy·2026
Same author

MARRVEL-MCP: An agentic interface for Mendelian disease discovery via tool-augmented context engineering.

American journal of human genetics·2026
Same author

NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection.

Scientific reports·2026
Same author

LA-MARRVEL: A Knowledge-Grounded, Language-Aware LLM Framework for Clinically Robust Rare Disease Gene Prioritization.

ArXiv·2026
Same author

Semi-Supervised Fatty Liver Classification Using Attention-Based Graph Neural Network Models.

Journal of Korean medical science·2026
Same author

ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.

medRxiv : the preprint server for health sciences·2025
Same journal

Improved prognostic survival models for pediatric medulloblastoma using high dimensional gene expression data.

BMC medical genomics·2026
Same journal

Identification of a novel pathogenic variant in MYLK in an Iranian family with non-syndromic familial aortic aneurysm and dissection by whole-exome sequencing and literature review.

BMC medical genomics·2026
Same journal

Genomic determinants of fluoroquinolone resistance in Escherichia coli in Nigeria: dominance of QRDR mutations and limited contribution of PMQR in a cross-sectional study.

BMC medical genomics·2026
Same journal

Crosstalk mediators implicated in the Stevens-Johnson Syndrome through gene regulatory network analysis.

BMC medical genomics·2026
Same journal

Familial lymphoma and genetic predisposition: an updated review.

BMC medical genomics·2026
Same journal

Discovery and validation of a prognostic SPP1/PLAU signature in HPV-negative oropharyngeal squamous cell carcinoma.

BMC medical genomics·2026
See all related articles

Related Experiment Video

Updated: Mar 16, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K

Integrative regression network for genomic association study.

Reddy Rani Vangimalla1, Hyun-Hwan Jeong1, Kyung-Ah Sohn2

  • 1Department of Information and Computer Engineering, Ajou University, Suwon, 443-749, Republic of Korea.

BMC Medical Genomics
|August 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an integrative regression network to uncover cancer genomic mechanisms by analyzing multi-layered genomic profiles. Fusing results from multiple regression techniques improves the identification of significant gene associations.

Keywords:
Genomic associationIntegrative analysisSimilarity fusion networkSparse regressionTCGA

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

2.7K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.8K

Related Experiment Videos

Last Updated: Mar 16, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.4K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

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

Published on: December 7, 2021

2.7K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.8K

Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Multi-layered genomic profiles offer insights into cancer mechanisms.
  • Conventional association tests struggle with indirect associations and data integration.
  • Analyzing gene expression and multi-omic features is crucial for understanding cancer.

Purpose of the Study:

  • To develop a novel framework, the integrative regression network, for identifying genomic associations across multiple high-dimensional profiles.
  • To address limitations of conventional methods by considering associations within and between genomic profiles.
  • To improve the reliability and interpretability of genomic association studies.

Main Methods:

  • Proposed an integrative regression network framework using high-dimensional regression techniques.
  • Constructed regression networks within profiles based on regression coefficients between different genomic layers.
  • Merged multiple sparse structured regression networks using similarity network fusion.

Main Results:

  • Applied four sparse structured regression methods on five cancer types from TCGA.
  • Demonstrated inconsistency across different regression methods, highlighting the need for integration.
  • Showcased that fusing networks via similarity measurements identified significant gene pairs and improved network topology.

Conclusions:

  • Validated the integrative regression network on TCGA multi-layered genomic data.
  • The method effectively identifies both strong and weak genomic signals by integrating diverse regression techniques.
  • The framework is extensible to incorporate results from different cancer types.