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

Drug Discovery: Overview01:26

Drug Discovery: Overview

10.7K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
10.7K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.6K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.6K
Dose-Response Relationship: Selectivity and Specificity01:25

Dose-Response Relationship: Selectivity and Specificity

9.4K
Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and...
9.4K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.1K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.1K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

259
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
259

You might also read

Related Articles

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

Sort by
Same author

Pathogen-specific host responses define distinct pneumonia endotypes in the human lung.

bioRxiv : the preprint server for biology·2026
Same author

Subclassification of Small Cell Lung Cancer Based on Gene Expression Signatures and Machine Learning.

Cancer research communications·2026
Same author

Mechanistic insights into the monotherapy and combination potential of FEN1 inhibition in cancer therapy.

Nucleic acids research·2025
Same author

AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.

Accounts of chemical research·2025
Same author

Liquid and Tissue Biopsies for Identifying MET Exon 14 Skipping NSCLC: Analyses from the Phase II VISION Study of Tepotinib.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

ctDNA Clearance as an Early Indicator of Improved Clinical Outcomes in Advanced NSCLC Treated with TKI: Findings from an Aggregate Analysis of Eight Clinical Trials.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025

Related Experiment Video

Updated: Dec 18, 2025

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

7.9K

Feature selection strategies for drug sensitivity prediction.

Krzysztof Koras1, Dilafruz Juraeva2, Julian Kreis2

  • 1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.

Scientific Reports
|June 12, 2020
PubMed
Summary

Prior knowledge-driven feature selection improves drug sensitivity prediction in cancer. This approach enhances personalized medicine by identifying key biological features for targeted therapies.

More Related Videos

Drug-induced Sensitization of Adenylyl Cyclase: Assay Streamlining and Miniaturization for Small Molecule and siRNA Screening Applications
09:39

Drug-induced Sensitization of Adenylyl Cyclase: Assay Streamlining and Miniaturization for Small Molecule and siRNA Screening Applications

Published on: January 27, 2014

13.0K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.1K

Related Experiment Videos

Last Updated: Dec 18, 2025

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

7.9K
Drug-induced Sensitization of Adenylyl Cyclase: Assay Streamlining and Miniaturization for Small Molecule and siRNA Screening Applications
09:39

Drug-induced Sensitization of Adenylyl Cyclase: Assay Streamlining and Miniaturization for Small Molecule and siRNA Screening Applications

Published on: January 27, 2014

13.0K
Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
16:02

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

3.1K

Area of Science:

  • Genomics
  • Computational Biology
  • Pharmacology

Background:

  • Personalized medicine aims to tailor cancer treatments, but predicting drug sensitivity is challenging.
  • Cancer cell drug sensitivity relies on complex interactions within a vast biological feature landscape.
  • Standard data-driven methods often struggle to identify the most relevant features for accurate prediction.

Purpose of the Study:

  • To compare data-driven versus prior knowledge-driven feature selection for drug sensitivity prediction.
  • To evaluate the impact of incorporating drug targets, pathways, and gene expression signatures.
  • To develop more interpretable and predictive models for cancer therapy design.

Main Methods:

  • Utilized the Genomics of Drug Sensitivity in Cancer (GDSC) dataset.
  • Assessed 2484 unique predictive models.
  • Compared standard feature selection against methods incorporating prior biological knowledge.

Main Results:

  • Prior knowledge-based feature selection outperformed standard methods for 23 drugs.
  • Linifanib showed the highest correlation (r=0.75) between observed and predicted response.
  • Integrating gene expression signatures with drug-specific features yielded optimal models for 60 drugs, notably Dabrafenib.
  • Small, knowledge-driven feature sets were highly predictive, especially for drugs targeting specific pathways.

Conclusions:

  • Prior knowledge integration significantly enhances drug sensitivity prediction models.
  • Feature selection strategies tailored to drug mechanisms (specific vs. general) improve model performance.
  • This approach facilitates the development of interpretable models crucial for guiding cancer therapy decisions.