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

Biostatistics: Overview01:20

Biostatistics: Overview

1.1K
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
1.1K
Stratified Sampling Method01:16

Stratified Sampling Method

16.1K
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...
16.1K
Classification of Illness01:17

Classification of Illness

9.3K
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...
9.3K
Cluster Sampling Method01:20

Cluster Sampling Method

15.5K
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...
15.5K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

715
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
715
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
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...
310

You might also read

Related Articles

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

Sort by
Same author

Deep docking, part 2: an amplified DDU platform for ultra-large virtual screening.

Chemical science·2026
Same author

A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.

Nature communications·2025
Same author

scMUSCL: multi-source transfer learning for clustering scRNA-seq data.

Bioinformatics (Oxford, England)·2025
Same author

Machine learning model identifies genetic predictors of cisplatin-induced ototoxicity in CERS6 and TLR4.

Computers in biology and medicine·2024
Same author

Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks.

Journal of chemical information and modeling·2024
Same author

Phenotype prediction from single-cell RNA-seq data using attention-based neural networks.

Bioinformatics (Oxford, England)·2024
Same journal

Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
Same journal

DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

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

8.1K

BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.

Sahand Khakabimamaghani1, Martin Ester

  • 1School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada, sahandk@sfu.ca.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 19, 2016
PubMed
Summary
This summary is machine-generated.

Stratified medicine (SM) advances personalized medicine by grouping patients. This study introduces B2PS, a Bayesian method, showing transcriptomic data improves patient stratification accuracy over genomic data.

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

16.5K

Related Experiment Videos

Last Updated: Mar 27, 2026

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

8.1K
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

16.5K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The shift towards personalized medicine highlights the importance of Stratified Medicine (SM).
  • Current patient stratification methods face challenges in data integration, comparison, and the use of probabilistic approaches.

Purpose of the Study:

  • To address limitations in current patient stratification methods.
  • To introduce and evaluate a novel integrative Bayesian biclustering method (B2PS) for patient stratification.
  • To investigate the utility of integrating different data types and the benefits of probabilistic methods.

Main Methods:

  • Developed B2PS, an integrative Bayesian biclustering method for patient stratification.
  • Proposed methods for evaluating the performance and results of patient stratification techniques.
  • Compared B2PS with a state-of-the-art method using experimental data.

Main Results:

  • B2PS demonstrated superior performance compared to a popular state-of-the-art method.
  • Bayesian approaches were shown to offer significant benefits for patient stratification.
  • Transcriptomic data proved to be a more effective basis for patient stratification than genomic data.

Conclusions:

  • The B2PS method offers an effective approach to patient stratification.
  • Integrative Bayesian methods enhance the accuracy of disease subtype detection.
  • Transcriptomic data holds greater potential for robust patient stratification in personalized medicine.