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

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...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

You might also read

Related Articles

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

Sort by
Same author

Network pharmacological prediction on metabolites of dominant endophytic strains from <i>Salvia plebeia</i> R. Br.

Frontiers in microbiology·2026
Same author

Detoxification of 3- and 15-acetyldeoxynivalenol and deoxynivalenol-3-glucoside by laccase Lac-W with acetosyringone.

Toxicon : official journal of the International Society on Toxinology·2026
Same author

<i>Staphylococcus epidermidis</i> prevents UV-induced skin aging by suppressing TLR3-mediated senescence.

Frontiers in immunology·2026
Same author

Pathogenesis of renal involvement in primary Sjögren's disease: convergence of multifactorial mechanisms on immune dysregulation.

Frontiers in immunology·2026
Same author

RNA Sequencing Explores Potential Mechanisms Underlying the Enhanced Aggressive Phenotype of HepG2 Cells Induced by Aged Zinc Oxide Nanoparticles.

International journal of nanomedicine·2026
Same author

CrisprPr: a hybrid-driven framework for CRISPR/Cas9 off-target prediction with analysis of prior-information updates.

Briefings in bioinformatics·2026

Related Experiment Video

Updated: May 10, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.7K

Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond.

Yicheng Gao1,2, Zhiting Wei1,2, Kejing Dong1,2

  • 1State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.

Nature Computational Science
|September 28, 2024
PubMed
Summary

This study introduces Subtask Decomposition Modeling for Genetic Perturbation Prediction (STAMP), an AI strategy that improves predicting cellular responses to genetic changes. STAMP enhances understanding of gene expression and interactions across cell lines.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.1K

Related Experiment Videos

Last Updated: May 10, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

20.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.1K

Area of Science:

  • Genomics
  • Computational Biology
  • Artificial Intelligence

Background:

  • Understanding cellular responses to genetic perturbations is crucial for biomedical research.
  • Predicting outcomes of single and multiple genetic perturbations across different cell lines presents significant challenges.

Purpose of the Study:

  • To introduce Subtask Decomposition Modeling for Genetic Perturbation Prediction (STAMP), an AI strategy for predicting genetic perturbation outcomes.
  • To address the limitations of existing methods in predicting gene expression changes and interactions.

Main Methods:

  • STAMP formulates genetic perturbation prediction as a subtask decomposition problem.
  • It progressively identifies differentially expressed genes, determines expression change directions, and estimates expression change magnitudes.

Main Results:

  • STAMP demonstrates substantial improvements over existing approaches in predicting genetic perturbation outcomes.
  • The method effectively identifies key regulatory genes and pathways, even with small sample sizes.
  • Precise genetic interactions of diverse types are revealed.

Conclusions:

  • STAMP offers a flexible and powerful AI strategy for genetic perturbation prediction and downstream applications.
  • The subtask decomposition approach enhances the accuracy and scope of predicting cellular responses to genetic modifications.