Jove
Visualize
Contact Us

Related Concept Videos

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase01:11

Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase

Genetic polymorphisms in drug targets have emerged as critical determinants of interindividual variability in drug response and toxicity. Pharmacogenomic investigations increasingly focus on identifying these variations to personalize and optimize therapeutic interventions. A drug target may be a receptor, enzyme, or signaling protein involved in pharmacologic responses or disease-related pathways. While early pharmacogenetic studies focused primarily on drug metabolism, current research...
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against specific...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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...

You might also read

Related Articles

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

Sort by
Same author

CAR-T Cell-Derived Exosomes and Cancer Immunotherapy: Advancing Production and Delivery Through Biofabrication.

Biofabrication·2026
Same author

Deep docking, part 2: an amplified DDU platform for ultra-large virtual screening.

Chemical science·2026
Same author

LOCALE: Local-Alignment Embeddings for Noise-Robust DNA Search at SRA Scale.

bioRxiv : the preprint server for biology·2026
Same author

Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment.

bioRxiv : the preprint server for biology·2026
Same author

LCPAN: efficient variation graph construction using locally consistent parsing.

Genome biology·2026
Same author

Microfluidic platforms for precision delivery of therapeutic cells in regenerative and personalized medicine.

Advanced drug delivery reviews·2026
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: May 31, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Optimally discriminative subnetwork markers predict response to chemotherapy.

Phuong Dao1, Kendric Wang, Colin Collins

  • 1School of Computing Science, Simon Fraser University.

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary

A new network-based algorithm, OptDis, uses protein-protein interaction (PPI) networks to predict cancer treatment response. This method improves upon existing subnetwork approaches, offering more stable and reproducible predictive markers for personalized cancer therapy.

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

Related Experiment Videos

Last Updated: May 31, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Research

Background:

  • Molecular profiles aid tumor classification, but predicting cancer treatment response remains challenging.
  • Integrating protein-protein interaction (PPI) data with gene expression profiles shows promise for developing predictive markers.
  • Novel approaches with improved generalizability are needed for treatment response prediction.

Purpose of the Study:

  • To introduce OptDis, a novel network-based classification algorithm for identifying optimally discriminative subnetwork markers.
  • To apply OptDis to predict drug response in breast cancer patients using PPI networks.
  • To evaluate OptDis's performance against existing subnetwork and single-gene methods.

Main Methods:

  • Developed OptDis, a network-based classification algorithm utilizing a color-coding technique.
  • Applied OptDis to published datasets of breast cancer patients undergoing combination chemotherapy.
  • Focused on protein-protein interaction (PPI) networks for subnetwork marker identification.

Main Results:

  • OptDis demonstrated improved and more stable performance compared to previous subnetwork and single-gene methods.
  • The algorithm identified predictive markers that were more reproducible across independent patient cohorts.
  • The identified subnetworks provided valuable insights into the biological mechanisms of treatment response.

Conclusions:

  • OptDis offers a robust and reproducible method for predicting cancer treatment response using molecular network data.
  • The algorithm enhances our understanding of the biological basis of therapy response.
  • OptDis represents a significant advancement in the development of personalized cancer treatments.