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

Frequency-dependent Selection01:21

Frequency-dependent Selection

24.5K
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.
24.5K
Epistasis Analysis01:09

Epistasis Analysis

6.2K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
6.2K
Genetic Screens02:46

Genetic Screens

5.9K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.9K
Genetic Variation01:25

Genetic Variation

1.6K
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
1.6K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

8.4K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
8.4K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.8K
3.8K

You might also read

Related Articles

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

Sort by
Same author

[Clinical application of Flow-through chimeric anterolateral thigh perforator flap].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery·2018
Same author

Biological Processes and Biomarkers Related to Frailty in Older Adults: A State-of-the-Science Literature Review.

Biological research for nursing·2018
Same author

A new era for stroke therapy: Integrating neurovascular protection with optimal reperfusion.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2018
Same author

Two-Year Randomized Clinical Trial of Adjunctive Minocycline Microspheres in Periodontal Maintenance.

Journal of dental hygiene : JDH·2018
Same author

Effect of family education program on cognitive impairment, anxiety, and depression in persons who have had a stroke: A randomized, controlled study.

Nursing & health sciences·2018
Same author

Eimeria tenella infection perturbs the chicken gut microbiota from the onset of oocyst shedding.

Veterinary parasitology·2018
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 5, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

1.1K

Confident difference criterion: a new Bayesian differentially expressed gene selection algorithm with applications.

Fang Yu1, Ming-Hui Chen2, Lynn Kuo3

  • 1Department of Biostatistics, University of Nebraska Medical Center, Omaha, 68198-4350, NE, USA. fangyu@unmc.edu.

BMC Bioinformatics
|August 8, 2015
PubMed
Summary
This summary is machine-generated.

New confident difference criterion methods efficiently identify differentially expressed (DE) genes using Bayesian analysis. These methods outperform existing approaches in both microarray and high-throughput sequencing studies, finding more clinically relevant genes.

More Related Videos

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

42.8K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K

Related Experiment Videos

Last Updated: Apr 5, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

1.1K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

42.8K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K

Area of Science:

  • Bioinformatics and Computational Biology
  • Statistical Genetics
  • Genomics and Transcriptomics

Background:

  • Bayesian methods are increasingly popular for analyzing high-dimensional gene expression data due to their ability to leverage information across genes.
  • Accurate estimation of gene expression levels and identification of differentially expressed (DE) genes are critical in biological research.
  • Developing efficient gene selection algorithms for Bayesian frameworks is essential for robust DE gene detection.

Purpose of the Study:

  • To propose two novel gene selection algorithms, the confident difference criterion methods, for general Bayesian models.
  • To evaluate the performance of these new methods against existing algorithms using simulations and real-world data.
  • To establish theoretical connections between the proposed methods and existing Bayesian approaches like Bayes factors.

Main Methods:

  • Extended the two-criterion idea to develop two confident difference criterion methods based on standardized differences in mean and variance of gene expression.
  • These methods assess the posterior probability of differential gene expression, classifying genes as DE if this probability is high.
  • Theoretical linkage established between the first method and Bayes factor approaches under a normal-normal model with equal variances.

Main Results:

  • The proposed confident difference criterion methods demonstrated superior performance compared to existing methods in simulation studies.
  • These methods effectively identified differentially expressed genes in both microarray and sequence-based high-throughput studies.
  • Application to a real dataset revealed that the confident difference criterion methods identified a greater number of clinically significant DE genes.

Conclusions:

  • The confident difference criterion method offers a new and efficient approach for identifying differentially expressed genes.
  • This methodology is applicable to both microarray and sequence-based high-throughput studies.
  • The proposed methods provide a valuable tool for robust gene selection in genomic data analysis.