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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by
Same author

The miR156h-TaSPL4-TaPIN18 Module Regulates Plant Architecture and Grain Size by Modulating Auxin Transport in Wheat.

Plant, cell & environment·2026
Same author

High prevalence and clinical impact of microbial co-detection in hospitalized children with human parainfluenza virus type 1.

Microbiology spectrum·2026
Same author

Efficacy and Safety of Tegoprazan-Based Dual, Triple, and Quadruple Therapies in First-Line Helicobacter pylori Eradication: A Prospective Randomized Controlled Trial.

Helicobacter·2026
Same author

Current Practices and Variations in the Use of FDG-PET and Bone Marrow Biopsy in Ewing Sarcoma Diagnosis: A Survey of Provider Recommendations.

Journal of pediatric hematology/oncology·2026
Same author

The prognostic significance of B7-H3 expression in patients with advanced colorectal cancer.

BMC cancer·2026
Same author

Enantioselective Synthesis of Bicyclic Lactone Enabled by Tunnel Reshaping of Polycyclic Ketone Monooxygenase.

Journal of agricultural and food chemistry·2026
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Multi-level mixed effects models for bead arrays.

Ryung S Kim1, Juan Lin

  • 1Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA. ryung.kim@einstein.yu.edu

Bioinformatics (Oxford, England)
|December 21, 2010
PubMed
Summary
This summary is machine-generated.

A new multi-level mixed effects model improves accuracy for high-throughput expression arrays by modeling bead-level variability. This approach reduces the false discovery rate in differential expression analysis, outperforming existing methods.

More Related Videos

A Paired Bead and Magnet Array for Molding Microwells with Variable Concave Geometries
11:42

A Paired Bead and Magnet Array for Molding Microwells with Variable Concave Geometries

Published on: January 28, 2018

Related Experiment Videos

Last Updated: Jun 5, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Paired Bead and Magnet Array for Molding Microwells with Variable Concave Geometries
11:42

A Paired Bead and Magnet Array for Molding Microwells with Variable Concave Geometries

Published on: January 28, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Bead arrays are increasingly used for high-throughput gene expression analysis.
  • Variability in bead counts and intensities across samples affects expression accuracy.
  • Accurate transcript intensity measurement is crucial for reliable biological interpretation.

Purpose of the Study:

  • To develop and validate a statistical model for analyzing bead array data.
  • To improve the accuracy of differential expression analysis in high-throughput expression arrays.
  • To provide a computational tool for efficient gene expression analysis.

Main Methods:

  • A multi-level mixed effects model was developed to account for bead-level variability.
  • The model's performance was evaluated using publicly available spike-in expression data.
  • Comparative analysis was performed against unweighted t-tests and other weighted methods.

Main Results:

  • The multi-level mixed effects model significantly reduced the false discovery rate for differential expression.
  • Evidence suggests the model outperforms existing methods, particularly under specific data conditions.
  • Theoretical insights were provided on the conditions favoring the proposed model's superiority.

Conclusions:

  • Modeling bead-level variability with a multi-level mixed effects model enhances differential expression analysis accuracy.
  • The developed statistical approach offers a robust alternative to conventional methods for bead array analysis.
  • A freely available software program facilitates efficient application of this advanced analytical method.