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

Experimental RNAi02:15

Experimental RNAi

6.2K
RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
6.2K
In-vitro Mutagenesis01:16

In-vitro Mutagenesis

14.1K
To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
14.1K

You might also read

Related Articles

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

Sort by
Same author

Electronics with switchable flexibility for 3D conforming neural interfaces.

Science advances·2026
Same author

Ferroptosis and the eye: bridging the gap between cell death and vision preservation.

Frontiers in immunology·2026
Same author

Chemical structure and P-selectin inhibition of two linear and highly regular homofucans from the brown alga Fucus vesiculosus.

Carbohydrate polymers·2026
Same author

Beyond apoptosis: platinum phototherapeutics overcome resistance by triggering diverse cell death pathways.

Chemical communications (Cambridge, England)·2026
Same author

C-ZIF7@Ag/EP Coating for Eco-Friendly Marine Protection: Near-Infrared-Responsive Self-Healing with High Antibiofouling Efficacy.

ACS applied materials & interfaces·2026
Same author

MLPH/RAB3A accelerates the differentiation of pancreatic stem cells to islet β-cells to control blood glucose in diabetic rats.

Organogenesis·2026

Related Experiment Video

Updated: Aug 7, 2025

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis
06:59

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis

Published on: August 11, 2010

12.1K

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs.

Tingting Zhao1,2, Guangyu Zhu3, Harsh Vardhan Dubey4

  • 1Department of Information Systems and Analytics, College of Business, Bryant University, Smithfield, 02917, RI, USA.

Briefings in Bioinformatics
|March 9, 2023
PubMed
Summary

This study introduces a novel method using model-X knockoffs and Deep Neural Networks to identify key gene expression changes in cellular response to perturbations. This approach enhances understanding of molecular pathways and aids in discovering new drug targets.

Keywords:
chemoinformaticsdeep learninggene expressiongenomicsknockoffs

More Related Videos

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits
09:17

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits

Published on: March 14, 2018

10.3K
Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes
18:38

Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes

Published on: September 25, 2013

12.1K

Related Experiment Videos

Last Updated: Aug 7, 2025

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis
06:59

A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis

Published on: August 11, 2010

12.1K
Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits
09:17

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits

Published on: March 14, 2018

10.3K
Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes
18:38

Large-scale Gene Knockdown in C. elegans Using dsRNA Feeding Libraries to Generate Robust Loss-of-function Phenotypes

Published on: September 25, 2013

12.1K

Area of Science:

  • Genomics and Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Understanding molecular pathways responding to genetic and environmental changes is crucial.
  • Identifying significant gene expression changes in large-scale perturbation experiments presents challenges due to unknown nonlinear relationships and high-dimensional variable selection.

Purpose of the Study:

  • To develop a robust method for identifying significant gene expression changes in multiple perturbation experiments.
  • To address challenges of unknown functional forms and high-dimensional variable selection in analyzing gene expression data.

Main Methods:

  • Utilized the model-X knockoffs framework combined with Deep Neural Networks.
  • Applied the method to the Library of Integrated Network-Based Cellular Signature (LINCS) datasets.
  • Ensured no assumptions on the functional form of gene expression response to perturbations and controlled false discovery rate.

Main Results:

  • Identified significant gene expression changes in response to various perturbations, including anthracycline, vorinostat, trichostatin-a, geldanamycin, and sirolimus.
  • Compared gene sets responding to small molecules to identify co-responsive pathways.
  • Successfully applied the novel method to a large, complex biological dataset.

Conclusions:

  • The developed method effectively identifies important gene expression responses without assuming functional forms.
  • Findings provide a better understanding of cellular mechanisms underlying disease and aid in identifying potential drug targets.
  • The approach offers a powerful tool for analyzing complex gene expression data from perturbation studies.