Related Experiment Video

Updated: Dec 26, 2025

Bioluminescence Imaging of an Immunocompetent Animal Model for Glioblastoma
09:17

Bioluminescence Imaging of an Immunocompetent Animal Model for Glioblastoma

Published on: January 15, 2016

15.3K

OSgbm: An Online Consensus Survival Analysis Web Server for Glioblastoma

Huan Dong1, Qiang Wang1, Ning Li1

  • 1Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.

Frontiers in Genetics
|March 11, 2020
PubMed

Keywords:
OSgbmclinical informationglioblastomaprognostic biomarkersurvival analysistranscriptome profiles

More Related Videos

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection08:23

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection

Published on: May 9, 2022

4.8K
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections05:45

Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections

Published on: July 31, 2017

10.0K

Related Concept Videos

Cancer Survival Analysis01:21

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...
592
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
325
Comparing the Survival Analysis of Two or More Groups01:20

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...
499