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

Modeling and Similitude01:12

Modeling and Similitude

307
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
307
Gauss's Law: Planar Symmetry01:27

Gauss's Law: Planar Symmetry

8.0K
A planar symmetry of charge density is obtained when charges are uniformly spread over a large flat surface. In planar symmetry, all points in a plane parallel to the plane of charge are identical with respect to the charges. Suppose the plane of the charge distribution is the xy-plane, and the electric field at a space point P with coordinates (x, y, z) is to be determined. Since the charge density is the same at all (x, y) - coordinates in the z = 0 plane, by symmetry, the electric field at P...
8.0K
Gauss's Law: Cylindrical Symmetry01:20

Gauss's Law: Cylindrical Symmetry

7.7K
A charge distribution has cylindrical symmetry if the charge density depends only upon the distance from the axis of the cylinder and does not vary along the axis or with the direction about the axis. In other words, if a system varies if it is rotated around the axis or shifted along the axis, it does not have cylindrical symmetry. In real systems, we do not have infinite cylinders; however, if the cylindrical object is considerably longer than the radius from it that we are interested in,...
7.7K
Gauss's Law: Spherical Symmetry01:26

Gauss's Law: Spherical Symmetry

7.6K
A charge distribution has spherical symmetry if the density of charge depends only on the distance from a point in space and not on the direction. In other words, if the system is rotated, it doesn't look different. For instance, if a sphere of radius R is uniformly charged with charge density ρ0, then the distribution has spherical symmetry. On the other hand, if a sphere of radius R is charged so that the top half of the sphere has a uniform charge density ρ1 and the bottom half...
7.6K
Typical Model Studies01:30

Typical Model Studies

396
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
396
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Synthetic Biology Toolkits and Metabolic Engineering Applied in <i>Corynebacterium glutamicum</i> for Biomanufacturing.

ACS synthetic biology·2021
Same author

Engineering of Synthetic Multiplexed Pathways for High-Level <i>N</i>-Acetylneuraminic Acid Bioproduction.

Journal of agricultural and food chemistry·2021
Same author

Dietary <i>Clostridium butyricum</i> and <i>Bacillus subtilis</i> Promote Goose Growth by Improving Intestinal Structure and Function, Antioxidative Capacity and Microbial Composition.

Animals : an open access journal from MDPI·2021
Same author

Effects of Plant Crown Shape on Microwave Backscattering Coefficients of Vegetation Canopy.

Sensors (Basel, Switzerland)·2021
Same author

Recent Progress in the Energy Harvesting Technology-From Self-Powered Sensors to Self-Sustained IoT, and New Applications.

Nanomaterials (Basel, Switzerland)·2021
Same author

Microscopy imaging of living cells in metabolic engineering.

Trends in biotechnology·2021

Related Experiment Video

Updated: Aug 4, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.7K

The systematic comparison between Gaussian mirror and Model-X knockoff models.

Shuai Chen1, Ziqi Li1, Long Liu2

  • 1Department of Health Statistics, School of Public Health, Shanxi Medical University, No 56 Xinjian South Road, Yingze District, Taiyuan, Shanxi Province, China.

Scientific Reports
|April 4, 2023
PubMed
Summary
This summary is machine-generated.

Gaussian Mirror (GM) and Model-X (MX) methods are powerful for biomarker discovery in high-dimensional biological data. GM offers more robust false discovery rate control, especially with correlated variables and smaller sample sizes, compared to MX-SO.

More Related Videos

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
12:14

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

21.8K
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.6K

Related Experiment Videos

Last Updated: Aug 4, 2025

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.7K
The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
12:14

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

21.8K
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.6K

Area of Science:

  • High-dimensional data analysis
  • Biomarker discovery
  • Statistical genetics

Background:

  • High-dimensional biological data offer vast potential for biomarker identification.
  • Lack of consensus exists on optimal analysis methods for such data.
  • Gaussian Mirror (GM) and Model-X (MX) knockoff methods are promising for biomarker detection but lack practical usage guidelines.

Purpose of the Study:

  • To systematically compare the performance of MX-based and GM methods for biomarker discovery.
  • To evaluate the impact of variable distribution, relatedness, and signal-to-noise ratio on method performance.
  • To assess the utility of GM and MX methods in identifying disease-associated biomarkers for Alzheimer's and Parkinson's diseases.

Main Methods:

  • Systematic performance comparison of MX-based (specifically MX-SO) and GM methods.
  • Evaluation across varying distributions of explanatory variables, correlation levels, and signal-to-noise ratios.
  • Application of selected methods to identify biomarkers for Alzheimer's disease (PET imaging trait) and Parkinson's disease (CSF T-tau).

Main Results:

  • MX with second-order approximate knockoffs (MX-SO) showed superior performance among MX-based methods.
  • MX-SO and GM exhibited similar statistical power and computational speed.
  • GM demonstrated superior robustness in controlling the false discovery rate (FDR), particularly with correlated variables and smaller sample sizes, where MX-SO's FDR control faltered.
  • Both methods successfully identified known disease-associated genes for Alzheimer's and Parkinson's diseases.

Conclusions:

  • Both MX-based and GM methods are powerful tools for analyzing large-scale biological data and identifying biomarkers.
  • GM provides more reliable FDR control, especially in challenging scenarios with correlated variables or limited sample sizes.
  • While MX-based methods may offer slightly higher power, GM's robustness makes it a more dependable choice for biomarker discovery in diverse biological datasets.