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

Genetic Screens02:46

Genetic Screens

5.3K
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...
5.3K
Genetic Drift03:33

Genetic Drift

42.0K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
42.0K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.7K
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...
7.7K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.2K
3.2K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

60.9K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
60.9K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.1K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.1K

You might also read

Related Articles

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

Sort by
Same author

HER2 assessment in locally advanced gastric cancer: comparing the results obtained with the use of two primary tumour blocks versus those obtained with the use of all primary tumour blocks.

Histopathology·2017
Same author

Inflammatory microRNA-194 and -515 attenuate the biosynthesis of chondroitin sulfate during human intervertebral disc degeneration.

Oncotarget·2017
Same author

Soil Acidification Aggravates the Occurrence of Bacterial Wilt in South China.

Frontiers in microbiology·2017
Same author

Is the Prophylactic Use of Hepatoprotectants Necessary in Anti-Tuberculosis Treatment?

Chemotherapy·2017
Same author

Light-induced aggregation of microbial exopolymeric substances.

Chemosphere·2017
Same author

Chemical Synthesis of (+)-Ryanodine and (+)-20-Deoxyspiganthine.

ACS central science·2017
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Nov 20, 2025

In Vivo Forward Genetic Screen to Identify Novel Neuroprotective Genes in Drosophila melanogaster
10:00

In Vivo Forward Genetic Screen to Identify Novel Neuroprotective Genes in Drosophila melanogaster

Published on: July 11, 2019

9.8K

MODEL-FREE FORWARD SCREENING VIA CUMULATIVE DIVERGENCE.

Tingyou Zhou1, Liping Zhu2, Chen Xu3

  • 1School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, P. R. China.

Journal of the American Statistical Association
|January 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces cumulative divergence (CD), a new, robust feature screening method for ultrahigh dimensional data. The CD-based approach effectively handles complex models and outliers, ensuring reliable feature selection.

Keywords:
Cumulative divergencefeature screeningforward screeninghigh dimensionalitysure screening propertyvariable selection

More Related Videos

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
09:05

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

22.7K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.3K

Related Experiment Videos

Last Updated: Nov 20, 2025

In Vivo Forward Genetic Screen to Identify Novel Neuroprotective Genes in Drosophila melanogaster
10:00

In Vivo Forward Genetic Screen to Identify Novel Neuroprotective Genes in Drosophila melanogaster

Published on: July 11, 2019

9.8K
Pooled CRISPR-Based Genetic Screens in Mammalian Cells
09:05

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

22.7K
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.3K

Area of Science:

  • Statistics
  • Machine Learning
  • Data Mining

Background:

  • Ultrahigh dimensional data analysis requires effective feature screening.
  • Existing methods struggle with complex models, high noise, and outliers.
  • Model misspecification is a common issue in current screening techniques.

Purpose of the Study:

  • To develop a novel, robust feature screening method for ultrahigh dimensional data.
  • To address limitations of existing methods, including sensitivity to outliers and model misspecification.
  • To introduce the cumulative divergence (CD) metric and a CD-based forward screening procedure.

Main Methods:

  • Developed a cumulative divergence (CD) metric.
  • Designed a CD-based forward screening procedure.
  • Incorporated joint effects of covariates and used a data-driven threshold for automatic feature selection.

Main Results:

  • The proposed CD-based screening is model-free and outlier-resistant.
  • The method accounts for joint effects among covariates.
  • Demonstrated the sure screening property under regularity conditions.
  • Validated performance through simulations and a real data example.

Conclusions:

  • The CD-based forward screening method offers a robust and effective solution for ultrahigh dimensional data.
  • Its model-free and outlier-resistant nature makes it highly practical.
  • The method automatically determines the optimal number of features to retain.