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

10.3K
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...
10.3K
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

115
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
115
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

6.0K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
6.0K
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

119
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
119
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

992
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
992
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

422
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
422

You might also read

Related Articles

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

Sort by
Same author

From cross cancer transcriptomics to therapeutics: WGX-50 target hub genes in breast cancer and non-small cell lung carcinoma.

Computational biology and chemistry·2026
Same author

PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction.

BMC genomics·2026
Same author

Spatial Mapping of Single Cells via Correlation and Importance Between Cells and Spots.

Interdisciplinary sciences, computational life sciences·2026
Same author

Artificial intelligence-driven anticancer peptide discovery.

iMetaOmics·2026
Same author

Targeted Nanodelivery of WGX50 and Curcumin via Gold Nanoparticles for Alzheimer's Therapy.

Journal of cellular and molecular medicine·2026
Same author

Editorial: Data-driven vaccine design for microbial-associated diseases.

Frontiers in immunology·2026

Related Experiment Video

Updated: Apr 25, 2026

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.5K

A hadoop-based method to predict potential effective drug combination.

Yifan Sun1, Yi Xiong1, Qian Xu1

  • 1State Key Laboratory of Microbial Metabolism and College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.

Biomed Research International
|August 23, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Hadoop-based approach for predicting effective drug combinations, significantly improving scalability and efficiency in big data processing for complex diseases.

More Related Videos

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

21.0K
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

3.4K

Related Experiment Videos

Last Updated: Apr 25, 2026

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.5K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

21.0K
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

3.4K

Area of Science:

  • Computational biology
  • Bioinformatics
  • Pharmacogenomics

Background:

  • Combination drugs offer improved efficacy and reduced side effects for complex diseases.
  • Exhaustive screening of drug combinations is computationally intensive and impractical.
  • Developing scalable prediction methods is crucial for advancing combination drug discovery.

Purpose of the Study:

  • To develop a novel, scalable Hadoop-based approach for predicting effective drug combinations.
  • To leverage the MapReduce programming model for enhanced computational efficiency.
  • To integrate gene expression data for accurate drug combination prediction.

Main Methods:

  • Implemented a Hadoop-based framework utilizing the MapReduce programming model.
  • Integrated multi-drug gene expression data for analysis.
  • Employed Support Vector Machines (SVM) and Naïve Bayesian classifiers on Hadoop for prediction.
  • Developed data preprocessing pipelines within the Hadoop ecosystem.

Main Results:

  • The Hadoop-based model demonstrated significantly higher efficiency in big data processing.
  • Satisfactory predictive performance was achieved with the proposed approach.
  • The scalability of the drug combination prediction algorithm was substantially improved.
  • The approach facilitates accelerated prediction of potential effective drug combinations.

Conclusions:

  • The proposed Hadoop-based approach offers an efficient and scalable solution for drug combination prediction.
  • This method can accelerate the discovery of novel therapeutic strategies for complex diseases.
  • The integration of big data technologies like Hadoop is vital for modern drug discovery pipelines.