Jove
Visualize
Contact Us

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

328
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...
328
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
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...
4.9K
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

5.5K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.5K

You might also read

Related Articles

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

Sort by
Same author

MVRBind: multi-view learning for RNA-small molecule binding site prediction.

Briefings in bioinformatics·2025
Same author

Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.

Briefings in bioinformatics·2025
Same author

Author Correction: Benchmarking machine learning methods for synthetic lethality prediction in cancer.

Nature communications·2025
Same author

DeepRSMA: a cross-fusion-based deep learning method for RNA-small molecule binding affinity prediction.

Bioinformatics (Oxford, England)·2024
Same author

CLigOpt: controllable ligand design through target-specific optimization.

Bioinformatics (Oxford, England)·2024
Same author

ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists.

Nature communications·2024
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 Experiment Video

Updated: Jun 10, 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

4.1K

Benchmarking machine learning methods for synthetic lethality prediction in cancer.

Yimiao Feng1,2, Yahui Long3, He Wang1

  • 1School of Information Science and Technology, ShanghaiTech University, Shanghai, China.

Nature Communications
|October 20, 2024
PubMed
Summary

Synthetic lethality (SL) prediction uses machine learning to find cancer drug targets. This study benchmarks 12 methods, finding data quality improvements boost performance and SLMGAE excels, though limitations remain for real-world applications.

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

155
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

6.7K

Related Experiment Videos

Last Updated: Jun 10, 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

4.1K
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

155
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

6.7K

Area of Science:

  • Computational biology
  • Genomics
  • Drug discovery

Background:

  • Synthetic lethality (SL) offers promising anticancer drug targets by exploiting cancer-specific dependencies.
  • Machine learning (ML) methods are increasingly used for SL prediction to complement experimental screening.
  • A comprehensive performance evaluation of existing ML-based SL prediction methods is lacking.

Purpose of the Study:

  • To systematically benchmark 12 recent ML methods for SL prediction.
  • To assess method performance across various data splitting, negative sampling, and task scenarios (classification and ranking).
  • To identify limitations and provide guidance for selecting and developing SL prediction techniques.

Main Methods:

  • Systematic benchmarking of 12 ML algorithms for SL prediction.
  • Evaluation across diverse data splitting strategies and negative sampling techniques.
  • Performance assessment on both classification and ranking tasks.

Main Results:

  • All evaluated ML methods showed improved performance with enhanced data quality, such as excluding computational SLs and using gene expression for negative sampling.
  • The SLMGAE method demonstrated superior performance among the tested algorithms.
  • Significant limitations were identified in realistic scenarios, including cold-start independent tests and context-specific SL prediction.

Conclusions:

  • Improving data quality is crucial for enhancing the performance of ML-based SL prediction.
  • SLMGAE is a promising method, but current ML approaches face challenges in real-world, context-specific applications.
  • The study provides valuable insights and resources for selecting and advancing ML techniques in SL virtual screening.