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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

223
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
223
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

463
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...
463
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

1.1K
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...
1.1K
Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.9K
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

297
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...
297
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

226
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
226

You might also read

Related Articles

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

Sort by
Same author

Glycemic response trajectories on metformin monotherapy in real-world diabetes care.

medRxiv : the preprint server for health sciences·2026
Same author

Robust ranking of renewable energy alternatives handling uncertainty using novel hesitant bi-fuzzy MEREC-MOORA and Dombi aggregation approach.

Scientific reports·2026
Same author

Patient Preference Phenotypes for Post-operative Anticoagulation After Hip or Knee Replacement: A Cross-sectional Survey Study.

Journal of general internal medicine·2026
Same author

The Impact of Social Vulnerability on Exercise Outcomes: A Longitudinal Study of Physical Function in Older People With HIV.

Journal of the International Association of Providers of AIDS Care·2026
Same author

Special issue: cell and gene causal inference in the design and analysis of gene therapy clinical trials.

Journal of biopharmaceutical statistics·2026
Same author

Mapping the last mile: Micro-stratification for sustained visceral leishmaniasis elimination in Bangladesh.

PLoS neglected tropical diseases·2026
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: Jan 9, 2026

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

CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.

Anirban Chakraborty1, Chloe Mattila1, Debashis Ghosh2

  • 1Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.

Biorxiv : the Preprint Server for Biology
|December 3, 2025
PubMed
Summary
This summary is machine-generated.

A new method, copula-based Bayesian kernel machine regression (CBKMR), accurately identifies cell type markers from omics data. This approach handles complex gene interactions and discrete outcomes, improving upon existing Bayesian kernel machine regression (BKMR) and machine learning methods.

Keywords:
Bayesian variable selectionBulk omicsGKMRGaussian Copulanearest neighbor GP approximationscRNA-seq

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.6K

Related Experiment Videos

Last Updated: Jan 9, 2026

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
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.6K

Area of Science:

  • Computational biology
  • Statistical genetics
  • Bioinformatics

Background:

  • High-throughput omics technologies provide comprehensive molecular data but identifying effective marker sets for cell types or disease states is difficult.
  • Existing methods like univariate tests miss gene dependencies, while machine learning approaches often lack feature selection and uncertainty quantification.
  • The Bayesian kernel machine regression (BKMR) framework captures nonlinearities and interactions but struggles with discrete outcomes.

Purpose of the Study:

  • To develop an improved Bayesian kernel machine regression model for identifying marker sets from omics data, particularly for discrete outcomes like cell types.
  • To enhance the scalability of the model for large single-cell RNA sequencing (scRNA-seq) datasets.
  • To compare the performance of the proposed model against existing methods in simulations and real-world applications.

Main Methods:

  • Proposed a copula-based Bayesian kernel machine regression (CBKMR) model using discrete marginals and a Gaussian copula for dependence.
  • Introduced a nearest-neighbor GP-based variant (NNCBKMR) for computational efficiency, reducing complexity from O(N^3) to nearly linear.
  • Evaluated CBKMR through simulation studies and applications to scRNA-seq datasets.

Main Results:

  • CBKMR demonstrated superior performance in capturing nonlinear effects and selecting markers compared to standard BKMR and ensemble machine learning methods.
  • The NNCBKMR variant achieved significant computational speed-up for large datasets.
  • CBKMR identified concise gene marker panels that corresponded well with expert-defined signatures, offering posterior uncertainty estimates.

Conclusions:

  • CBKMR provides a robust and scalable framework for marker discovery in high-dimensional omics data, especially for discrete outcomes.
  • The model improves upon existing BKMR formulations and machine learning techniques by integrating nonlinearity, interactions, and uncertainty quantification.
  • CBKMR facilitates more reliable and interpretable identification of cell type and disease-associated markers.