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

DNA Microarrays02:34

DNA Microarrays

23.0K
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...
23.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

709
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
709

You might also read

Related Articles

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

Sort by
Same author

Identification of sequence variants in genetic disease-causing genes using targeted next-generation sequencing.

PloS one·2012
Same author

Inhibition of matrine against gastric cancer cell line MNK45 growth and its anti-tumor mechanism.

Molecular biology reports·2011
Same author

Evolution of activation patterns during long-duration ventricular fibrillation in pigs.

American journal of physiology. Heart and circulatory physiology·2011
Same author

A new feruloyl amide derivative from the fruits of Tribulus terrestris.

Natural product research·2011
Same author

High-amylose rice improves indices of animal health in normal and diabetic rats.

Plant biotechnology journal·2011
Same author

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

Statistical methods in medical research·2011

Related Experiment Video

Updated: Apr 3, 2026

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.1K

Supervised group Lasso with applications to microarray data analysis.

Shuangge Ma1, Xiao Song, Jian Huang

  • 1Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu

BMC Bioinformatics
|February 24, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces a novel supervised group Lasso method to identify key genes and gene clusters from microarray data for disease diagnosis and prognosis. The approach improves prediction accuracy by leveraging inherent gene expression data structures.

More Related Videos

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
11:04

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

10.6K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.8K

Related Experiment Videos

Last Updated: Apr 3, 2026

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

11.1K
RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
11:04

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Published on: May 19, 2019

10.6K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.8K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray gene expression data analysis is crucial for disease diagnosis and prognosis.
  • Gene expression data exhibits cluster structures representing co-regulated genes with coordinated functions.
  • Existing statistical methods often overlook this inherent cluster structure in gene selection.

Purpose of the Study:

  • To develop a supervised group Lasso approach for gene selection and predictive model building that incorporates cluster structure.
  • To enhance the identification of influential genes and gene clusters for improved disease classification and survival analysis.

Main Methods:

  • A supervised group Lasso method is proposed, integrating K-means clustering and Gap statistic for unsupervised data.
  • Genes are first screened within clusters using Lasso, followed by cluster selection via group Lasso.
  • V-fold cross-validation and leave-one-out cross-validation are employed for parameter tuning and performance evaluation.

Main Results:

  • The method successfully identifies a small set of influential gene clusters and key genes within them.
  • Applied to cancer and lymphoma microarray data, the approach demonstrated superior prediction performance compared to existing methods.
  • The analysis included disease classification and survival analysis, validating the method's versatility.

Conclusions:

  • The supervised group Lasso approach effectively utilizes gene expression data's cluster structure for robust gene selection.
  • This method offers improved predictive power for disease diagnosis, prognosis, and survival analysis.
  • The findings highlight the importance of considering co-regulation patterns in gene expression analysis.