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

Classification of Illness01:17

Classification of Illness

8.0K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.0K
Multiple Bar Graph01:07

Multiple Bar Graph

8.2K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
8.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

136
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...
136
Causality in Epidemiology01:21

Causality in Epidemiology

1.0K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.0K
Cancer Survival Analysis01:21

Cancer Survival Analysis

472
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
472
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

652
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
652

You might also read

Related Articles

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

Sort by
Same author

Dual-drug-loaded nanohydrogel for intraoperative local application: sequential release-mediated spatiotemporal targeting of diverse secondary injury mechanisms to improve long-term prognosis in traumatic brain injury.

BMC medicine·2026
Same author

Ultra-Stable 2D Magneto-Fluorescent Probe-Mediated Multiplex Immunochromatographic Assay for Precise Bedside Detection of Sepsis.

ACS nano·2026
Same author

A two-stage signal enhancement method integrating the Gaussian mixture model and adaptive rolling ball technique for ultrasensitive fluorescent immunochromatographic detection.

Analytical methods : advancing methods and applications·2026
Same author

An integrated green strategy based on deep eutectic solvents and ultrasound for efficient polyphenol profiling and antioxidant evaluation of dendrobium officinale.

Ultrasonics sonochemistry·2026
Same author

Integrated cervicocerebral ultrasound-based hemodynamic compensation scoring for anterior-circulation steno-occlusive disease: validation against CT perfusion staging.

Quantitative imaging in medicine and surgery·2026
Same author

MRCNet: Motion Reasoning Chain for Cross Modal Video Camouflaged Object Detection.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Sep 30, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.8K

Multi-Modal Graph Learning for Disease Prediction.

Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu

    IEEE Transactions on Medical Imaging
    |March 14, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Multi-modal Graph Learning (MMGL) framework for disease prediction. MMGL effectively integrates multi-modal data by adaptively learning graph structures, improving diagnostic accuracy.

    More Related Videos

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K
    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

    7.7K

    Related Experiment Videos

    Last Updated: Sep 30, 2025

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
    07:35

    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

    Published on: October 13, 2023

    1.8K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K
    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

    7.7K

    Area of Science:

    • Biomedical informatics
    • Machine learning
    • Graph representation learning

    Background:

    • Graph-based approaches excel in multi-modal medical data analysis for disease prediction.
    • Existing methods often rely on manual graph construction and overlook inter-modal correlations, limiting diagnostic accuracy.

    Purpose of the Study:

    • To propose an end-to-end Multi-modal Graph Learning (MMGL) framework for enhanced disease prediction using multi-modal data.
    • To address limitations of manual graph construction and capture complex inter-modal relationships.

    Main Methods:

    • Developed a modality-aware representation learning strategy to aggregate features by leveraging modality correlation and complementarity.
    • Introduced adaptive graph learning to capture latent graph structures, jointly optimized with the prediction model.
    • Ensured applicability to inductive learning scenarios for unseen data.

    Main Results:

    • The proposed MMGL framework demonstrated superior performance on two disease prediction tasks.
    • Adaptive graph learning effectively revealed intrinsic connections among samples, enhancing predictive power.

    Conclusions:

    • MMGL offers a robust and effective approach for multi-modal disease prediction.
    • The framework's ability to learn latent graph structures and integrate diverse data modalities leads to improved diagnostic outcomes.