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

Cancer Survival Analysis01:21

Cancer Survival Analysis

812
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...
812
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.2K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

692
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...
692
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

6.7K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
6.7K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

695
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,...
695

You might also read

Related Articles

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

Sort by
Same author

Caregiver-Associated Physical Activity Patterns, Dietary Behaviors and Interventional Beliefs in Individuals with Down Syndrome: Insights from a Large European Survey.

Nutrients·2026
Same author

Understanding Obesity in Individuals with Down Syndrome: Caregiver Perceptions, Awareness, and Motivation.

Nutrients·2026
Same author

De novo design of RNA pseudoknots with deep learning.

bioRxiv : the preprint server for biology·2026
Same author

A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era.

Journal of chemical theory and computation·2026
Same author

Knowledge preservation in the era of big science and AI: strategies for sustainable scientific research.

Nature communications·2026
Same author

Fusing imaging and metabolic modeling via multimodal deep learning in ovarian cancer.

Cell systems·2026
Same journal

What role does the Notch signaling pathway play in exercise-related metabolic and neurological adaptations? A molecular-to-systems perspective.

Frontiers in physiology·2026
Same journal

Variation in skin barrier function throughout smoltification in Atlantic salmon (<i>Salmo salar</i>).

Frontiers in physiology·2026
Same journal

Correction: What role does the Notch signaling pathway play in exercise-related metabolic and neurological adaptations? A molecular-to-systems perspective.

Frontiers in physiology·2026
Same journal

Effect of high intensity interval Nordic walking and strength training on selected biomarkers of metabolic syndrome in postmenopausal women with abdominal obesity: a quasi-experimental studies.

Frontiers in physiology·2026
Same journal

The interplay between sexual activity, athletic performance, and recovery in athletes: a narrative review.

Frontiers in physiology·2026
Same journal

The alveolar edema equation.

Frontiers in physiology·2026
See all related articles

Related Experiment Video

Updated: Mar 18, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Cancer Markers Selection Using Network-Based Cox Regression: A Methodological and Computational Practice.

Antonella Iuliano1, Annalisa Occhipinti2, Claudia Angelini1

  • 1Istituto per le Applicazioni del Calcolo "Mauro Picone," Consiglio Nazionale delle Ricerche Naples, Italy.

Frontiers in Physiology
|July 6, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces network-based Cox regression models to integrate biological data for cancer survival prediction. These methods improve biomarker discovery by considering gene networks, aiding clinical applications.

Keywords:
Cox modelcancergene expressionhigh-dimensionalitynetworkregularizationsurvival

More Related Videos

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

995
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

844

Related Experiment Videos

Last Updated: Mar 18, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

995
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

844

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • International initiatives like TCGA and ICGC generate large-scale cancer genomic datasets.
  • Cox regression models are used for survival analysis but struggle with correlated omics data and group biomarker selection.
  • Network-based approaches offer advantages but are underutilized in life sciences.

Purpose of the Study:

  • To provide a methodological framework for network-based Cox regression in cancer research.
  • To demonstrate integrating biological knowledge with omics data for biomarker discovery and survival prediction.
  • To present a novel permutation-based approach for validating cancer gene signatures and networks.

Main Methods:

  • Discussion of three network-based Cox regression algorithms: Net-Cox, AdaLnet, and fastcox.
  • Integration of biological knowledge with multi-omics data using these algorithms.
  • Application of a new permutation-based method for robust validation of identified networks and signatures.

Main Results:

  • Demonstrated utility of network-based Cox regression for analyzing cancer survival data.
  • Successful identification of cancer biomarker networks by combining biological information and omics data.
  • Validation of network significance through simulations and real case studies.

Conclusions:

  • Network-based Cox regression models offer a powerful approach to integrate biological knowledge for cancer survival analysis.
  • The proposed methodology provides a clear computational framework for investigating cancer regulatory networks.
  • This work facilitates the translation of cancer research findings into clinical applications.