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 Experiment Videos

Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed

Qais Al Hajri1, Sajal Dash2, Wu-Chun Feng1,2

  • 1Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24060, USA.

Scientific Reports
|February 8, 2020
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A preliminary study on the effect of quantified cranial osteopathic manipulation on wild-type and transgenic rat models of Alzheimer's disease.

Journal of Alzheimer's disease reports·2025
Same author

ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science.

Frontiers in high performance computing·2025
Same author

Scalable and Maintainable Distributed Sequence Alignment Using Spark.

IEEE transactions on computational biology and bioinformatics·2025
Same author

An approach for developing a blood-based screening panel for lung cancer based on clonal hematopoietic mutations.

PloS one·2024
Same author

A Process Evaluation of a Mobile App for Medical Students Aimed at Increasing Resilience and Decreasing Stigma in Mental Health.

Cureus·2024
Same author

Common outpatient diagnoses and associated treatments logged by osteopathic medical students within a geriatric population.

Journal of osteopathic medicine·2024
Same journal

Correction: A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms.

Scientific reports·2026
Same journal

Poly(bromophenol blue)/CoSn(OH)<sub>6</sub> cubic particles modified pencil graphite electrode for electrochemical determination of diphenhydramine.

Scientific reports·2026
Same journal

Dietary Chlorella, Spirulina, and acidifier modulate jejunal cytokine-related gene expression in broiler chickens.

Scientific reports·2026
Same journal

Perceived physical activity barriers in university students: associations with fatigue and eating behaviours.

Scientific reports·2026
Same journal

Refuge limitation structures habitat use in agricultural landscapes: evidence from Sunda pangolins.

Scientific reports·2026
Same journal

Lightweight stateless transaction verification with outsourced witness updates for UTXO blockchains.

Scientific reports·2026
See all related articles
This summary is machine-generated.

Researchers developed a faster algorithm to identify multi-hit gene combinations in cancer, improving diagnosis and paving the way for targeted therapies. This computational advance aids in understanding cancer

Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Effective cancer treatments are limited due to the complex genetic mutations underlying different cancer instances.
  • Identifying combinations of mutated genes (multi-hit combinations) is crucial for understanding cancer etiology.
  • Previous algorithms were computationally intensive, limiting the analysis to two-hit combinations.

Purpose of the Study:

  • To enhance an algorithm for identifying multi-hit gene combinations, specifically three or more genetic mutations.
  • To improve computational efficiency for analyzing complex cancer mutation data.
  • To enable the identification of potential driver mutations for targeted cancer therapies.

Main Methods:

  • Developed a compressed binary matrix representation for genetic data.

Related Experiment Videos

  • Optimized the algorithm for parallel processing on a graphics processing unit (GPU).
  • Compared the GPU implementation's speed against a CPU-based implementation.

    • Main Results:

    • The GPU-optimized algorithm achieved an estimated 12,144-fold speed increase for 3-hit combination identification.
    • Identified 3-hit gene combinations differentiated tumor from normal samples with 90% sensitivity and 93% specificity.
    • Demonstrated the potential to identify driver mutations from mutation distribution patterns.

    Conclusions:

    • The enhanced GPU algorithm efficiently identifies complex multi-hit gene combinations in cancer.
    • These findings offer insights into cancer origins and support the development of targeted combination therapies.
    • Further experimental validation is recommended to confirm driver mutations and therapeutic strategies.