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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.1K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.1K
Classification of Illness01:17

Classification of Illness

8.4K
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...
8.4K
Sampling Plans01:23

Sampling Plans

780
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
780
Cluster Sampling Method01:20

Cluster Sampling Method

13.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.8K
Survival Tree01:19

Survival Tree

311
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...
311
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

409
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
409

You might also read

Related Articles

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

Sort by
Same author

Hemodynamic Assessment in Newborn Infants With Sepsis: A Prospective Observational Study.

Cureus·2026
Same author

Risk Factors Associated With Febrile Seizures in Young Children: Clinical, Biochemical, and Genetic Perspectives.

Cureus·2026
Same author

The Transitional Liver Clinic: Study protocol for a stepped-wedge cluster randomized trial.

Hepatology communications·2026
Same author

Caregiver Involvement and Burden and Patient Quality of Life in Patients With Decompensated Cirrhosis.

The American journal of gastroenterology·2026
Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026
Same author

Danilo Bzdok.

Neuron·2026

Related Experiment Video

Updated: Dec 13, 2025

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

16.0K

Inferring disease subtypes from clusters in explanation space.

Marc-Andre Schulz1,2, Matt Chapman-Rounds3,4, Manisha Verma5,4

  • 1Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany. marc.schulz@rwth-aachen.de.

Scientific Reports
|August 1, 2020
PubMed
Summary

This study introduces a novel method for identifying disease subtypes by analyzing AI model explanations, outperforming traditional data clustering. This approach enhances subtype discovery in complex biomedical data.

More Related Videos

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

2.0K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.2K

Related Experiment Videos

Last Updated: Dec 13, 2025

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

16.0K
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

2.0K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.2K

Area of Science:

  • Biomedical data analysis
  • Artificial intelligence in medicine
  • Computational biology

Background:

  • Identifying disease subtypes and biomarkers is crucial for personalized medicine.
  • High-dimensional biomedical data presents challenges for human and machine analysis of subtypes.
  • Current methods struggle to effectively discover latent disease subtypes.

Purpose of the Study:

  • To develop a new computational approach for discovering disease subtypes.
  • To leverage AI model explanations for improved subtype identification.
  • To demonstrate the efficacy of clustering in explanation space for subtype discovery.

Main Methods:

  • Trained a diagnostic classifier (healthy vs. diseased) on biomedical data.
  • Extracted instance-wise explanations for the classifier's predictions.
  • Performed cluster analysis on the distribution of instances in the explanation space.
  • Compared clustering on explanations to classical clustering on original data.

Main Results:

  • Cluster analysis on model explanations substantially outperformed classical clustering.
  • This method demonstrated superior performance on UK Biobank brain imaging and Cancer Genome Atlas transcriptome data.
  • The explanation space effectively amplified differences, aiding subtype discovery.

Conclusions:

  • Clustering in explanation space is a powerful method for inferring disease subtypes.
  • The approach offers a generalizable framework for various subtype identification tasks.
  • This method enhances the discovery of latent subtypes in complex biomedical datasets.