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

Epistasis Analysis01:09

Epistasis Analysis

6.0K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
6.0K
Multiple Allele Traits01:49

Multiple Allele Traits

38.4K
The Concept of Multiple Allelism
38.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

375
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
375
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.3K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.3K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

902
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...
902
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

603
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
603

You might also read

Related Articles

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

Sort by
Same author

AI-Assisted Disease Monitoring Using Stretchable Polymer-Based Sensors.

ACS applied materials & interfaces·2023
Same author

Vibrio harveyi co-infected with Cryptocaryon irritans to orange-spotted groupers Epinephelus coioides.

Fish & shellfish immunology·2023
Same author

JAC4 Inhibits EGFR-Driven Lung Adenocarcinoma Growth and Metastasis through CTBP1-Mediated JWA/AMPK/NEDD4L/EGFR Axis.

International journal of molecular sciences·2023
Same author

Obtaining non-digestible polysaccharides from distillers' grains of Chinese baijiu after extrusion with enhanced antioxidation capability.

International journal of biological macromolecules·2023
Same author

Comprehensive analysis of prognostic value and immunotherapy prospect of brain cytoplasmic RNA1 in hepatocellular carcinoma.

World journal of gastrointestinal oncology·2023
Same author

BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation.

Computers in biology and medicine·2023

Related Experiment Video

Updated: Mar 1, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K

A fast and exhaustive method for heterogeneity and epistasis analysis based on multi-objective optimization.

Xiong Li1

  • 1School of Software, East China Jiaotong University, Nanchang 330013, China.

Bioinformatics (Oxford, England)
|May 26, 2017
PubMed
Summary

This study introduces ESMO, a novel computational method for epistasis analysis that efficiently detects genetic interactions and population heterogeneity in complex diseases. ESMO offers a faster, more reproducible approach for genetic association studies.

More Related Videos

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.7K
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

743

Related Experiment Videos

Last Updated: Mar 1, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.7K
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

743

Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Existing epistasis analysis methods suffer from ignoring heterogeneity, high computational costs, and performance volatility, limiting their use in complex disease studies.
  • These limitations lead to poor reproducibility and low statistical power in genetic association studies.

Purpose of the Study:

  • To propose ESMO, a fast scheme for accelerating exhaustive searching using multi-objective optimization.
  • To concurrently analyze heterogeneity and epistasis phenomena in complex diseases.

Main Methods:

  • ESMO combines mutual entropy and Bayesian network approaches for evaluating epistatic SNP combinations.
  • An adaptive framework using non-dominant sort and top k selection (time complexity O(k*M*N)) handles disease heterogeneity.
  • Acceleration strategies include trading space for time, calculation sharing, and parallel computing.

Main Results:

  • ESMO demonstrated rapid epistasis analysis capabilities in comparative experiments.
  • The method effectively detected heterogeneity in complex population structures.

Conclusions:

  • ESMO provides a computationally efficient and robust solution for epistasis analysis.
  • The nonparametric and model-free nature of ESMO enhances its applicability in diverse genetic studies.