Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genetic Lingo

Genetic Lingo

Genetic Screens

Genetic Screens

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 result in visible changes...

Genome-wide Association Studies-GWAS

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...

You might also read

Related Articles

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

Sort by

Same author

Inattention in Pediatric Attention-Deficit/Hyperactivity Disorder and Anxiety: Neurophysiological Evidence for Distinct and Overlapping Cognitive Mechanisms.

Journal of attention disorders·2026

Same author

Redefining Documentation Quality in the Age of Ambient Artificial Intelligence Scribes.

Annals of internal medicine·2026

Same author

Ambient AI Scribes and the Quintuple Aim: What Is Counted-and What Matters.

JAMA·2026

Same author

Pharmacist care for attention-deficit/hyperactivity disorder electronic medication refills: A cluster randomized trial.

Journal of managed care & specialty pharmacy·2025

Same author

AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence.

JAMA·2025

Same author

Pharmacist vs physician management of e-visit requests for COVID-19 medication: A randomized clinical trial.

Journal of managed care & specialty pharmacy·2025

Same journal

Improving Overall Risk Ranking via Subgroup-Level Information Borrowing in Survival Risk Stratification.

Statistics and its interface·2026

Same journal

High-dimensional Bayesian mediation analysis with adaptive Laplace priors.

Statistics and its interface·2026

Same journal

Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.

Statistics and its interface·2025

Same journal

Variable selection for doubly robust causal inference.

Statistics and its interface·2025

Same journal

Smooth online parameter estimation for time varying VAR models with application to rat local field potential activity data.

Statistics and its interface·2025

Same journal

A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.

Statistics and its interface·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

LASSO-Patternsearch algorithm with application to ophthalmology and genomic data.

Weiliang Shi¹, Grace Wahba, Stephen Wright

¹Department of Statistics, University of Wisconsin, 1300 University Avenue, Madison WI 53706,

Statistics and Its Interface

|October 15, 2008

Summary

This summary is machine-generated.

The LASSO-Patternsearch algorithm efficiently identifies risk factor patterns in demographic and genomic studies. This method uncovers important interactions for diseases like myopia and rheumatoid arthritis.

More Related Videos

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

Related Experiment Videos

Last Updated: Jun 29, 2026

Pattern-based Search of Epigenomic Data Using GeNemo

Pattern-based Search of Epigenomic Data Using GeNemo

Published on: October 8, 2017

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

Area of Science:

Biostatistics
Genomics
Epidemiology

Background:

Identifying complex patterns of multiple risk factors is crucial in demographic and genomic research.
Existing methods may struggle with a large number of candidate patterns and high-dimensional data.

Purpose of the Study:

To introduce the LASSO-Patternsearch algorithm for efficient identification of multiple dichotomous risk factor patterns.
To handle scenarios with numerous potential patterns, prioritizing those with significant impact.

Main Methods:

Utilizes a LASSO (Least Absolute Shrinkage and Selection Operator) approach for initial pattern reduction.
Employs a novel computational algorithm to manage a vast number of unknowns simultaneously.
Further refines patterns using generalized linear models and a modified GACV (Generalized Akaike Information Criterion) tuning procedure for model selection.

Main Results:

Successfully applied to myopia data from the Beaver Dam Eye Study, revealing physiologically relevant interacting risk factors.
Demonstrated effectiveness on a Rheumatoid Arthritis generative model, efficiently recovering higher-order patterns from genomic-scale attribute vectors.

Conclusions:

The LASSO-Patternsearch algorithm provides an efficient and powerful tool for pattern discovery in complex datasets.
The method shows significant potential for application in both epidemiological and large-scale genomic studies.