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

Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

457
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
457
DC Generator01:19

DC Generator

1.5K
An alternator converts mechanical energy into electrical energy that varies sinusoidally, resulting in AC current. Meanwhile, a DC generator converts mechanical energy into electrical energy, which are DC pulses with the same polarity. The construction of a DC generator is similar to that of an alternator, except that the pair of slip rings is replaced by a single split ring, also called a commutator. The commutator functions like a periodic rotary switch; it changes the contacts with the...
1.5K
Survival Tree01:19

Survival Tree

212
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...
212
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.0K
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...
1.0K
Diamagnetic Shielding of Nuclei: Local Diamagnetic Current01:14

Diamagnetic Shielding of Nuclei: Local Diamagnetic Current

1.1K
An applied magnetic field causes the electrons present in the molecule to circulate, setting up a local diamagnetic current within the molecule. The local diamagnetic current arising from circulating sigma-bonding electrons induces a magnetic field, Blocal that opposes the applied magnetic field, B0. The effective magnetic field experienced by these nuclei is given by the difference between the applied and local magnetic fields in a phenomenon called local diamagnetic shielding. Essentially,...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Sparse dimensionality reduction for analyzing single-cell-resolved interactions.

Bioinformatics advances·2026
Same author

Impact of catheter-directed thrombolysis on the socio-economic burden of pulmonary embolism in Germany: a cost-effectiveness analysis.

European heart journal. Quality of care & clinical outcomes·2026
Same author

Identifying Biomedical Entities for Datasets in Scientific Articles: 4-Step Cache-Augmented Generation Approach Using GPT-4o and PubTator 3.0.

JMIR formative research·2025
Same author

Sparse dimensionality reduction for analyzing single-cell-resolved interactions.

Bioinformatics (Oxford, England)·2025
Same author

Exploring Feature Preferences for a Treatment-Accompanying App in Patients Undergoing Radiation Therapy: Cross-Sectional Study.

JMIR cancer·2025
Same author

Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.

Briefings in bioinformatics·2025
Same journal

Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.

BMC medical research methodology·2026
Same journal

Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.

BMC medical research methodology·2026
Same journal

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same journal

Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.

BMC medical research methodology·2026
Same journal

Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.

BMC medical research methodology·2026
Same journal

An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers.

BMC medical research methodology·2026
See all related articles

Related Experiment Video

Updated: Nov 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.4K

Deep generative models in DataSHIELD.

Stefan Lenz1, Moritz Hess2, Harald Binder2

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany. lenz@imbi.uni-freiburg.de.

BMC Medical Research Methodology
|April 4, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a privacy-preserving method using artificial data generated by deep Boltzmann machines (DBMs) for distributed medical research. This approach enables joint analysis of sensitive patient data without compromising privacy, enhancing collaborative studies.

Keywords:
Biomedical research/methodsDeep learningDistributed systemPrivacy/statistics and numerical data

Related Experiment Videos

Last Updated: Nov 10, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.4K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Individual patient data is optimal for medical statistics but often inaccessible due to privacy regulations.
  • Germany restricts pooling routine hospital data for research without patient consent, highlighting data access challenges.

Purpose of the Study:

  • To develop a privacy-preserving method for joint analysis of distributed medical data.
  • To enable the use of artificial data generated from sensitive patient information for research.

Main Methods:

  • Utilized DataSHIELD software for privacy-preserving analysis of distributed data.
  • Employed deep Boltzmann machines (DBMs) as generative models to create artificial patient data.
  • Implemented DBMs using the Julia 'BoltzmannMachines' package, integrated with R-based DataSHIELD.

Main Results:

  • Successfully created artificial datasets preserving complex patterns from distributed individual patient data.
  • Demonstrated feasibility by conducting a distributed analysis on synthetic genetic variant data using DBMs.
  • Compared DBMs with other generative models (VAEs, GANs, MVI) for utility and disclosure in distributed settings.

Conclusions:

  • The implementation extends DataSHIELD with artificial data generation capabilities for advanced analyses like deep learning.
  • Showcased the flexibility of DataSHIELD in integrating external algorithms from languages like Julia.
  • The developed methodology facilitates privacy-preserving collaborative research on distributed sensitive datasets.