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

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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genomics02:02

Genomics

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...

You might also read

Related Articles

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

Sort by
Same author

Harmonized metagenomic signatures of the gut microbiome reveal robust species, functions, and strain links to inflammatory bowel disease.

Gastroenterology·2026
Same author

Circulating imidazole propionate and coronary heart disease risk: interplay between histidine intake, fiber, and gut microbiome.

BMC medicine·2026
Same author

Item recognition is associated with gut microbiota composition in healthy humans.

Learning & memory (Cold Spring Harbor, N.Y.)·2026
Same author

Shotgun Metagenomic Profiling of the Gut Virome in Prodromal and Confirmed Parkinson's Disease.

Annals of neurology·2026
Same author

Long-lasting gut microbiome and fecal metabolome alterations after colorectal adenoma removal and their relationship to colorectal cancer.

Cell host & microbe·2026
Same author

Dietary sulfur amino acids enhance anti-tumor immunity in colon cancer via an NKT cell-XCL1-cDC1 circuit.

Immunity·2026

Related Experiment Video

Updated: May 26, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Optimized application of penalized regression methods to diverse genomic data.

Levi Waldron1, Melania Pintilie, Ming-Sound Tsao

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.

Bioinformatics (Oxford, England)
|December 14, 2011
PubMed
Summary

Penalized regression methods like LASSO and Elastic Net are crucial for genomic data analysis. Optimal application requires careful tuning, as demonstrated by simulations and real-world cancer and metagenomic data applications.

More Related Videos

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

Related Experiment Videos

Last Updated: May 26, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

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

Area of Science:

  • Bioinformatics and Biostatistics
  • Genomic Data Analysis
  • High-Dimensional Data

Background:

  • Penalized regression methods are widely used for high-dimensional feature selection and prediction in bioinformatics.
  • Optimal application strategies for genomic data remain undetermined despite well-understood theoretical properties.

Purpose of the Study:

  • To compare the performance of LASSO, Ridge, and Elastic Net penalties for prediction and variable selection in high-dimensional genomic data.
  • To provide guidelines for the optimal application of penalized regression in genomic studies.

Main Methods:

  • Simulated contrasting scenarios of correlated high-dimensional survival data.
  • Compared LASSO, Ridge, and Elastic Net penalties.
  • Applied methods to cancer patient survival prediction (microarray data) and obesity classification (metagenomic data).

Main Results:

  • Two-dimensional tuning of Elastic Net penalties is necessary to differentiate its performance from LASSO or Ridge regression.
  • Univariate pre-filtering negatively impacted Elastic Net's prediction performance in scenarios favoring LASSO.
  • Demonstrated successful application in predicting cancer patient survival and classifying individuals based on metagenomic data.

Conclusions:

  • Optimized guidelines for applying penalized regression to genomic data are provided.
  • Emphasizes the importance of proper tuning and avoiding pre-filtering for optimal prediction performance.
  • The pensim R package offers a parallelized implementation for regression and data simulation.