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

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by
Same author

Environment and reproductive health in China: challenges and opportunities.

Environmental health perspectives·2012
Same author

Posttransplant mortality risk assessment for adult-to-adult right-lobe living donor liver recipients with benign end-stage liver disease.

Scandinavian journal of gastroenterology·2012
Same author

Sodium nitrite protects against kidney injury induced by brain death and improves post-transplant function.

Kidney international·2012
Same author

OIC-A006-loaded true bone ceramic heals rabbit critical-sized segmental radial defect.

Die Pharmazie·2012
Same author

Liquid chromatography-mass spectrometric multiple reaction monitoring-based strategies for expanding targeted profiling towards quantitative metabolomics.

Current drug metabolism·2012
Same author

Structural and functional characterization of mature forms of metalloprotease E495 from Arctic sea-ice bacterium Pseudoalteromonas sp. SM495.

PloS one·2012

Related Experiment Video

Updated: Jun 18, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction

Mingyue Liu1,2, Yu Tian1, Yuchao Jia1

  • 1Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.

Genome Medicine
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a new computational framework, DISCERN, to predict cancer drug responses at the single-cell level using genetic interactions. CellGIdb offers a resource for exploring these interactions and their role in cancer therapy.

Keywords:
Drug predictionSingle-cell transcriptomicsSynthetic lethalitySynthetic viability

More Related Videos

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae
12:13

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Published on: April 5, 2015

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence
09:54

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Published on: March 8, 2020

Related Experiment Videos

Last Updated: Jun 18, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
08:46

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae
12:13

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Published on: April 5, 2015

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence
09:54

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence

Published on: March 8, 2020

Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Genetic interactions like synthetic lethality (SL) and synthetic viability (SV) are key to understanding cancer vulnerabilities and drug resistance.
  • Predicting drug response at single-cell resolution using SL and SV is currently challenging.

Purpose of the Study:

  • To develop a computational framework for inferring single-cell drug sensitivity based on genetic interactions.
  • To create a comprehensive atlas of cell-type-specific SL and SV networks across various human cancers.
  • To establish an interactive portal for exploring these genetic interaction networks.

Main Methods:

  • Construction of cell-type-specific SL and SV networks from scRNA-seq data across 14 human cancers.
  • Development of DISCERN (Drug response Inference from Single-Cell gEnetic inteRactioNs), a computational framework utilizing malignant cell-specific genetic interactions.
  • Establishment of CellGIdb, an interactive portal for accessing cell-type-specific genetic interaction networks.

Main Results:

  • Reconstructed cell-type-specific genetic interaction networks, identifying shared and distinct patterns.
  • Demonstrated prognostic value and correlation with immunotherapy response for SL and SV interactions in malignant cells and T cells.
  • Showcased DISCERN's effective inference of tumor cell-specific drug sensitivity in lung and breast cancers, outperforming existing methods.
  • Highlighted CellGIdb's utility in exploring genetic interactions' roles in drug response and immunotherapy.

Conclusions:

  • Developed a comprehensive atlas and the DISCERN framework for single-cell drug response interpretation via genetic interactions.
  • The CellGIdb resource facilitates further research into cell-type-specific vulnerabilities for improved cancer therapy.