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

Survival Tree01:19

Survival Tree

126
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
126
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.7K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.7K
Aggregates Classification01:29

Aggregates Classification

356
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
356
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

102
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...
102
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

685
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
685
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

201
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
201

You might also read

Related Articles

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

Sort by
Same author

scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.

Nature communications·2026
Same author

Predicting Parkinson's disease trajectory using clinical and functional MRI features: A reproduction and replication study.

PloS one·2025
Same author

Longitudinal prognosis of Parkinson's outcomes using causal connectivity.

NeuroImage. Clinical·2024
Same author

Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI.

Journal of neural engineering·2023
Same author

BLENDS: Augmentation of Functional Magnetic Resonance Images for Machine Learning Using Anatomically Constrained Warping.

Brain connectivity·2022
Same author

Glaucoma Diagnosis Through the Integration of Optical Coherence Tomography/Angiography and Machine Learning Diagnostic Models.

Clinical ophthalmology (Auckland, N.Z.)·2022

Related Experiment Video

Updated: Aug 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

646

Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and

Kevin P Nguyen, Alex H Treacher, Albert A Montillo

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    We introduce Adversarially-Regularized Mixed Effects Deep learning (ARMED) models to address clustering in scientific data. ARMED improves deep learning performance and feature interpretability by separating cluster-specific effects.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    592

    Related Experiment Videos

    Last Updated: Aug 4, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    646
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    592

    Area of Science:

    • Computational Biology
    • Machine Learning
    • Statistical Modeling

    Background:

    • Natural science datasets often exhibit clustering (e.g., by study site, subject, batch), violating independence assumptions.
    • Clustering can lead to spurious associations, poor model fitting, and confounded analyses in deep learning models.
    • Traditional statistical methods like mixed effects models handle clustering by separating fixed and random effects.

    Purpose of the Study:

    • To propose a general-purpose framework, Adversarially-Regularized Mixed Effects Deep learning (ARMED), for deep learning models with clustered data.
    • To enable deep learning models to learn cluster-invariant features while capturing cluster-specific variations.
    • To allow the application of random effects to clusters not seen during model training.

    Main Methods:

    • Introduced non-intrusive additions to existing neural networks: an adversarial classifier for cluster-invariant features, a random effects subnetwork for cluster-specific features.
    • Developed a method to apply random effects to unseen clusters during training.
    • Applied the ARMED framework to dense, convolutional, and autoencoder neural networks across four diverse datasets.

    Main Results:

    • ARMED models demonstrated superior ability to distinguish confounded from true associations in simulations compared to prior techniques.
    • ARMED learned more biologically plausible features in clinical applications (dementia prognosis/diagnosis, live-cell imaging).
    • ARMED matched or improved performance on both seen (5-28% relative improvement) and unseen clusters (2-9% relative improvement) versus conventional models.

    Conclusions:

    • The ARMED framework provides a robust solution for deep learning on clustered scientific data.
    • ARMED enhances feature learning, improves model performance, and offers interpretability by quantifying inter-cluster variance.
    • This approach effectively addresses a critical limitation in applying deep learning to real-world scientific datasets.