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

14.0K
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...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

2.9K
2.9K

You might also read

Related Articles

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

Sort by
Same author

Raising the Bar in Graph OOD Generalization: Invariant Learning beyond Explicit Environment Modeling.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Impact of Fostamatinib on Inflammatory Biomarkers in Hospitalized Patients With COVID-19.

Critical care explorations·2026
Same author

Quantitative decoupling of the key factors and mechanisms of Fenton-pretreated membrane fouling mitigation via machine learning-assisted causal inference.

Water research·2026
Same author

Visible-wavelength vectorial holography based on an MIM metasurface.

Discover nano·2026
Same author

Management of hip fracture in older adults with cognitive impairment: a narrative review.

Frontiers in public health·2026
Same author

Development and internal validation of a clinical prediction model for 1-year recurrence after first-ever ischemic stroke.

Frontiers in neurology·2026

Related Experiment Video

Updated: Aug 31, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.8K

A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression.

Hai-Hui Huang1, Hao Rao1, Rui Miao2

  • 1Provincial Demonstration Software Institute, Shaoguan University, Shaoguan, China.

BMC Bioinformatics
|August 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new meta-analysis framework using data augmentation and elastic data shared lasso (DSL) to improve gene expression analysis. The method enhances prediction and gene selection for identifying robust disease signatures, aiding early diagnosis and treatment.

Keywords:
Gene expressionIntegrative analysisMeta-analysisRegularizationVariable selection

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

38.3K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K

Related Experiment Videos

Last Updated: Aug 31, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

5.8K
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

38.3K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression analysis is crucial for understanding biological mechanisms but often suffers from unrepeatable findings.
  • Small sample sizes and low signal-to-noise ratios in gene expression datasets limit the reliability of traditional analyses.
  • Meta-analysis of multiple datasets offers a solution to enhance the robustness of gene expression findings.

Purpose of the Study:

  • To develop a novel meta-analysis framework to improve gene expression data analysis.
  • To address challenges of small sample sizes and low signal-to-noise ratios in gene expression datasets.
  • To enhance the prediction and gene selection performance in meta-analyses.

Main Methods:

  • Proposed a novel meta-analysis framework comprising a data augmentation strategy and the elastic data shared lasso (DSL) method.
  • Developed a data augmentation technique utilizing cross-platform normalization to add "perturbations" to gene expression datasets.
  • Introduced the DSL method, which balances individual and shared models, improving performance with highly correlated features.

Main Results:

  • Simulation experiments demonstrated high prediction and gene selection performance of the proposed meta-analysis method.
  • Applied the framework to non-small cell lung cancer (NSCLC) blood gene expression data.
  • Identified key tumor-related genes and a robust set of disease-related gene signatures.

Conclusions:

  • The proposed novel and effective meta-analysis method enhances biological research by integrating information from multiple gene expression datasets.
  • The method shows potential for identifying reliable gene signatures for early diagnosis, prognosis, and targeted therapy in diseases like NSCLC.
  • This framework offers a robust approach to overcome limitations in current gene expression analysis.