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

Response Surface Methodology01:16

Response Surface Methodology

583
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
583
The R Chart01:02

The R Chart

358
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
358
Drug Product Stability01:16

Drug Product Stability

217
The long-term stability of drug products is critical to ensuring their quality, safety, and effectiveness over time. Stability directly influences a product's ability to maintain its intended characteristics, ensuring it performs as expected during its intended shelf life. Key attributes such as drug potency, impurities, dissolution, and other physicochemical measures of performance are tested to assess stability. These parameters indicate how well the product retains its quality over time and...
217
Microtubule Instability02:17

Microtubule Instability

6.0K
Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
6.0K
RNA Stability01:53

RNA Stability

35.6K
Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
35.6K
Interpreting R Charts01:22

Interpreting R Charts

314
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
314

You might also read

Related Articles

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

Sort by
Same author

The Role of Adjuvant Chemotherapy in High-Risk Stage II Colon Cancer with Microsatellite Instability-High or DNA Mismatch Repair Deficiencies: A Multicenter Pooled Analysis (KCSG-CO24-03).

Cancer research and treatment·2026
Same author

MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis.

IEEE transactions on visualization and computer graphics·2026
Same author

Visual Analytics using Tensor Unified Linear Comparative Analysis.

IEEE transactions on visualization and computer graphics·2026
Same author

Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods.

IEEE transactions on visualization and computer graphics·2026
Same author

Visual Extraction of Interaction Patterns Guided by Hierarchical Clustering and Process Mining.

IEEE transactions on visualization and computer graphics·2025
Same author

UMATO: Bridging Local and Global Structures for Reliable Visual Analytics With Dimensionality Reduction.

IEEE transactions on visualization and computer graphics·2025

Related Experiment Video

Updated: Jan 9, 2026

A Semi-Quantitative Drug Affinity Responsive Target Stability DARTS assay for studying Rapamycin/mTOR interaction
05:28

A Semi-Quantitative Drug Affinity Responsive Target Stability DARTS assay for studying Rapamycin/mTOR interaction

Published on: August 27, 2019

17.5K

GhostUMAP2: Measuring and Analyzing $(r,d)$-Stability of UMAP.

Myeongwon Jung, Takanori Fujiwara, Jaemin Jo

    IEEE Transactions on Visualization and Computer Graphics
    |December 4, 2025
    PubMed
    Summary

    Uniform Manifold Approximation and Projection (UMAP) results can be unstable due to its random processes. We introduce (r,d)-stability to quantify and analyze projection stability, improving UMAP

    Area of Science:

    • Data Science
    • Machine Learning
    • Dimensionality Reduction

    Background:

    • Uniform Manifold Approximation and Projection (UMAP) is a popular dimensionality reduction technique.
    • The stochastic nature of UMAP's optimization can lead to unstable projection results.
    • The impact of this stochasticity on UMAP's reliability is not well understood.

    Purpose of the Study:

    • To introduce a novel framework, (r,d)-stability, for analyzing the stability of UMAP projections.
    • To quantify the impact of stochastic elements, such as initial positions and negative sampling, on UMAP results.
    • To provide tools and guidelines for assessing and improving UMAP projection stability.

    Main Methods:

    • Introduction of 'ghosts' (duplicate data points) to represent potential positional variations due to stochasticity.

    More Related Videos

    The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe
    08:53

    The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe

    Published on: December 3, 2016

    7.3K
    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
    06:37

    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

    Published on: September 17, 2021

    5.0K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    A Semi-Quantitative Drug Affinity Responsive Target Stability DARTS assay for studying Rapamycin/mTOR interaction
    05:28

    A Semi-Quantitative Drug Affinity Responsive Target Stability DARTS assay for studying Rapamycin/mTOR interaction

    Published on: August 27, 2019

    17.5K
    The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe
    08:53

    The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe

    Published on: December 3, 2016

    7.3K
    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
    06:37

    Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

    Published on: September 17, 2021

    5.0K
  • Definition of (r,d)-stability based on the confinement of ghost projections within a specified radius.
  • Development of an adaptive dropping scheme for efficient computation of ghost projections.

    • Main Results:

    • The proposed (r,d)-stability framework effectively analyzes the impact of stochasticity in UMAP.
    • An adaptive dropping scheme reduces runtime by up to 60% while preserving ~90% of unstable points.
    • A visualization tool facilitates interactive exploration of data point projection stability.

    Conclusions:

    • The (r,d)-stability framework provides a robust method for evaluating UMAP projection reliability.
    • The adaptive dropping scheme offers significant computational efficiency gains.
    • The developed tools and guidelines aid researchers in achieving more stable and interpretable UMAP results.