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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.1K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.1K
Cancer Survival Analysis01:21

Cancer Survival Analysis

458
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
458
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.9K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
5.9K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K

You might also read

Related Articles

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

Sort by
Same author

Identification of genes associated with smell dysfunction in Parkinson's disease.

Clinical parkinsonism & related disorders·2026
Same author

Deubiquitinases as prognostic biomarker and potential drug target for gynecological cancers.

International journal of clinical oncology·2025
Same author

Machine Learning Approaches for the Identification of Genetic Interactions.

Methods in molecular biology (Clifton, N.J.)·2025
Same author

Potential role of Hematopoietic PBX-Interacting Protein (HPIP) in trophoblast fusion and invasion: Implications in pre-eclampsia pathogenesis.

Cellular signalling·2025
Same author

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

PLoS computational biology·2024
Same author

Mariner: explore the Hi-Cs.

Bioinformatics (Oxford, England)·2024
Same journal

Nanotechnology-Stem Cell Strategies in 3D Glioblastoma Organoid: Targeting Glioma Stem Cells Within a Complex Tumor Microenvironment.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Sep 18, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.3K

R-Based Protocols to Predict Synthetic Lethal Interactions in Cancers Using Machine Learning Tools.

Anubha Dey1, Manjari Kiran2

  • 1Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India.

Methods in Molecular Biology (Clifton, N.J.)
|June 24, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) models are revolutionizing cancer research by predicting synthetic lethal interactions, which are key to developing targeted therapies. This approach aids in personalized cancer treatment by identifying effective drug targets.

Keywords:
CancerGenetic interactionsMachine learningSynthetic lethality

More Related Videos

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells
07:23

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Published on: May 30, 2025

653
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Sep 18, 2025

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
07:40

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

Published on: May 27, 2021

4.3K
Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells
07:23

Dual CRISPR-Interference Strategy for Targeting Synthetic Lethal Interactions Between Non-Coding RNAs in Cancer Cells

Published on: May 30, 2025

653
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Artificial intelligence (AI) and machine learning (ML) are transforming healthcare research, including cancer studies.
  • Targeted therapy, personalized for cancer patients, offers advantages over traditional chemotherapy and radiation.
  • Genetic interactions (GI) are crucial for understanding drug sensitivity and resistance in targeted cancer therapies.

Purpose of the Study:

  • To explore machine learning models for predicting synthetic lethal interactions in cancer.
  • To summarize the strengths and weaknesses of various ML classifiers for GI prediction.
  • To provide practical R-based protocols for implementing ML-based synthetic lethal interaction prediction algorithms.

Main Methods:

  • Review and discussion of machine learning models applicable to synthetic lethal interaction prediction.
  • Analysis of advantages and disadvantages of different ML classifiers.
  • Inclusion of R-based step-by-step protocols for executing ML algorithms.

Main Results:

  • Identified and discussed various ML models for predicting synthetic lethal interactions.
  • Summarized the pros and cons of these predictive models.
  • Provided executable R protocols for practical application of ML in GI prediction.

Conclusions:

  • Machine learning offers powerful tools for predicting genetic interactions relevant to cancer.
  • Understanding synthetic lethality through ML aids in the development of effective targeted cancer therapies.
  • The provided protocols enable researchers to apply ML models for predicting synthetic lethal interactions.