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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Frequency-dependent Selection01:21

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

You might also read

Related Articles

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

Sort by
Same author

Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms.

Data science in science·2026
Same author

Emotional words evoke region- and valence-specific patterns of concurrent neuromodulator release in human thalamus and cortex.

Cell reports·2026
Same author

A transcriptional program associated with neurotransmission in the living human brain.

Molecular psychiatry·2026
Same author

Electrodiffusion in cardiac intercalated disc nanostructures alters cell-cell action potential transmission via ephaptic coupling: A model study.

The Journal of physiology·2025
Same author

BISON: bi-clustering of spatial omics data with feature selection.

Bioinformatics (Oxford, England)·2025
Same author

Bayesian covariate-dependent graph learning with a dual group spike-and-slab prior.

Biometrics·2025

Related Experiment Video

Updated: Jun 6, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data.

Francesco C Stingo1, Marina Vannucci

  • 1Department of Statistics, Rice University, Houston, TX 77005, USA.

Bioinformatics (Oxford, England)
|December 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying disease biomarkers using gene expression data. By incorporating gene networks, the approach improves biomarker selection accuracy and aids biological interpretation.

More Related Videos

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Related Experiment Videos

Last Updated: Jun 6, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Discriminant analysis is crucial for classifying experimental units.
  • Gene expression data is widely used for phenotype classification.
  • Identifying biomarkers is essential for understanding biological processes.

Purpose of the Study:

  • To develop a novel method for biomarker identification using gene expression data.
  • To integrate gene-gene network information into a discriminant analysis model.
  • To enhance the accuracy of variable selection in classification tasks.

Main Methods:

  • A conjugate normal discriminant model (linear and quadratic) was employed.
  • Stochastic search variable selection was implemented using a Markov Chain Monte Carlo (MCMC) algorithm.
  • A Markov Random Field (MRF) prior was utilized to incorporate gene-gene network topology.

Main Results:

  • The proposed method, utilizing the MRF prior, demonstrated improved selection accuracy compared to independent priors in simulations.
  • Application to benchmark gene expression datasets identified significant markers and enhanced prediction accuracy.
  • Integration of biological knowledge via the MRF prior facilitated the identification of genes with strong discriminatory power.

Conclusions:

  • The developed method effectively identifies important biomarkers from gene expression data.
  • Incorporating gene-gene network information enhances biomarker discovery and interpretation.
  • This approach offers a powerful tool for integrating prior biological knowledge into statistical models for genomics research.