Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

Cancer Survival Analysis

Cancer Survival Analysis

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...

Survival Tree

Survival Tree

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 survival tree begins...

You might also read

Related Articles

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

Sort by

Same author

Cell Cycle Sensing Shapes Human T Cell Fate and Exhaustion Programs.

bioRxiv : the preprint server for biology·2026

Same author

Wavelet Decomposition-Based Genomic Analysis of the Human Electrocardiogram.

medRxiv : the preprint server for health sciences·2026

Same author

Structure-preserving multivariate hypothesis testing for mass spectrometry imaging and single-cell data.

Bioinformatics (Oxford, England)·2026

Same author

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition in metastatic TNBC.

Nature cancer·2026

Same author

Prognostic pan-cancer and single-cancer models: A large-scale analysis using a real-world clinico-genomic database.

PloS one·2026

Same author

Glaucoma Classification Through SSVEP-Derived ON- and OFF-Pathway Features.

Translational vision science & technology·2026

Same journal

The human claustrum supports cognitive networks for externally and internally driven task demands.

PLoS biology·2026

Same journal

Unusual decay: Recombination loss leads to splicing errors in green algae.

PLoS biology·2026

Same journal

Angptl5 restricts primitive hematopoiesis by promoting retinoic acid signaling in zebrafish.

PLoS biology·2026

Same journal

Engineered bipaternal mice reveal the consequences of life without a maternal genomic contribution.

PLoS biology·2026

Same journal

Multiple adhesion molecules act together in oligodendrocyte-mediated axonal selection and myelin formation.

PLoS biology·2026

Same journal

Splicing deficiency is driven by genomic erosion in non-recombining algal mating-type chromosomes.

PLoS biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Semi-supervised methods to predict patient survival from gene expression data.

Eric Bair¹, Robert Tibshirani

¹Department of Statistics, Stanford University, Palo Alto, USA. ebair@stanford.edu

|April 20, 2004

Summary

This summary is machine-generated.

This study introduces a new method for identifying cancer subtypes using gene expression and clinical data. This approach aids in more accurate cancer diagnosis and survival prediction for future patients.

More Related Videos

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Jun 23, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Accurate cancer diagnosis relies on genetic profiling, but existing methods often require pre-identified subtypes.
Current subtype identification methods lack broad applicability across diverse datasets.
Clinical data, such as patient survival time, is often available even when subtypes are unknown.

Purpose of the Study:

To develop a robust procedure for identifying cancer subtypes using both gene expression and clinical data.
To create diagnostic tools applicable across various cancer types, irrespective of known subtypes.
To improve the accuracy of diagnosing future patients based on identified subtypes and survival data.

Main Methods:

Integration of gene expression data with patient clinical information (e.g., survival time).
Development of novel procedures for unsupervised identification of cancer subtypes.
Application of developed procedures to publicly available cancer datasets.

Main Results:

Successfully identified distinct cancer subtypes in multiple datasets.
Developed diagnostic procedures that accurately predict patient survival.
Demonstrated the utility of the combined gene expression and clinical data approach.

Conclusions:

The developed procedures offer a powerful tool for cancer subtype discovery and diagnosis.
This approach enhances diagnostic accuracy by leveraging both genetic and clinical information.
The findings have significant potential for improving cancer patient management and treatment strategies.