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

Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Sampling Plans01:23

Sampling Plans

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...

You might also read

Related Articles

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

Sort by
Same author

Association of Early Knee Extension Range of Motion Deficits With Cartilage T2 Relaxation Following Anterior Cruciate Ligament Reconstruction.

The American journal of sports medicine·2026
Same author

Machine learning-driven QSAR modeling combined with single cell transcriptomics identifies novel drug targets for lung cancer.

Journal of translational medicine·2026
Same author

Biomarkers for cancer screening, diagnosis and targeted therapeutic approaches.

Advances in cancer research·2026
Same author

An explainable-AI framework reveals novel lncRNAs specific for breast cancer subtypes.

Frontiers in bioinformatics·2026
Same author

CEST MRI assessment of HIV-1-associated neurometabolic impairments in a humanized mouse model.

NeuroImmune pharmacology and therapeutics·2026
Same author

VST-DAVis: an R Shiny application and web-browser for spatial transcriptomics data analysis and visualization.

Bioinformatics advances·2026

Related Experiment Video

Updated: May 11, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.5K

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data.

Sushil K Shakyawar1, Balasrinivasa R Sajja2, Jai Chand Patel1

  • 1Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, United States.

Bioinformatics Advances
|May 3, 2024
PubMed
Summary

iCluF integrates multiomic data (mRNA, miRNA, DNA methylation) for patient stratification. This machine learning approach effectively clusters patients into subtypes across 30 cancers, improving disease management.

More Related Videos

Author Spotlight: Enhancing Nuclei Isolation for Multiome Sequencing in Challenging Tumor Microenvironments
03:49

Author Spotlight: Enhancing Nuclei Isolation for Multiome Sequencing in Challenging Tumor Microenvironments

Published on: October 13, 2023

1.1K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K

Related Experiment Videos

Last Updated: May 11, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.5K
Author Spotlight: Enhancing Nuclei Isolation for Multiome Sequencing in Challenging Tumor Microenvironments
03:49

Author Spotlight: Enhancing Nuclei Isolation for Multiome Sequencing in Challenging Tumor Microenvironments

Published on: October 13, 2023

1.1K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Patient stratification is essential for treating complex diseases like cancer.
  • Multiomic technologies provide deep molecular insights but generate complex data.
  • Robust data integration tools are needed for machine learning-based patient stratification.

Purpose of the Study:

  • To develop and validate iCluF, a novel computational tool for integrating multiomic data.
  • To improve patient stratification and subtype discovery using machine learning.

Main Methods:

  • Iterative integration of mRNA, miRNA, and DNA methylation data.
  • Utilizing pairwise patient similarity matrices and message passing.
  • Clustering patients into subtypes based on integrated omics profiles.

Main Results:

  • iCluF significantly improved survival profile distinctions in 8581 patients across 30 cancers (TCGA).
  • Accurately predicted four intrinsic subtypes of Breast Invasive Carcinomas (ARI=0.72, FM=0.83).
  • DNA methylation data emerged as the most influential feature for subtype identification.

Conclusions:

  • iCluF is an effective tool for multiomic data integration and patient stratification.
  • The method demonstrates broad applicability to various diseases with multiomic datasets.
  • DNA methylation plays a critical role in defining cancer subtypes.