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

407
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...
407
Genomics02:02

Genomics

36.7K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
36.7K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

211
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,...
211
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

237
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...
237
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.0K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.0K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

442
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
442

You might also read

Related Articles

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

Sort by
Same author

United States benign gallbladder disease mortality, 1999-2024: rising cholecystitis and persistent demographic and geographic disparities.

Surgical endoscopy·2026
Same author

Venetoclax in combination with cytarabine with or without idarubicin or azacitidine in children, adolescents, and young adults with relapsed or refractory acute myeloid leukaemia (VENAML): a multicentre, phase 1 expansion study.

The Lancet. Haematology·2026
Same author

DNA methylation-mediated downregulation of MEIS2 correlates with tumor development and progression in gastric cancer.

Biochimica et biophysica acta. Molecular basis of disease·2026
Same author

Appendectomy and colorectal cancer: a mini review from the perspective of gut microbiota and mucosal immunity.

Frontiers in cellular and infection microbiology·2026
Same author

Assessing the Global, Regional, and National Burden of Childhood Lower Respiratory Infections From Nonexclusive Breastfeeding, 1990-2021: Global Burden of Disease, Injuries, and Risk Factors Study 2021 Analysis and Implications for Public Health Strategies.

Journal of human lactation : official journal of International Lactation Consultant Association·2026
Same author

The ASH HematOmics Program supports integrative analysis of genomic and clinical data in hematologic diseases.

Blood·2026

Related Experiment Video

Updated: Aug 6, 2025

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

1.4K

Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics.

Xueyuan Cao1, Abdelrahman H Elsayed2,3, Stanley B Pounds4

  • 1College of Nursing, University of Tennessee Health Science Center, Memphis, TN, USA.

Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
PubMed
Summary

Researchers developed novel statistical methods, including GRIN, ALEX, and PROMISE, to analyze pediatric cancer multi-omics data. These methods identify cancer-driving genes and predict patient prognosis, aiding biological discovery and clinical trial development.

Keywords:
Gene expressionGenome-wide associationGenomic lesionProjectionRandom intervalT-ALL

More Related Videos

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.6K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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

Published on: May 17, 2019

9.0K

Related Experiment Videos

Last Updated: Aug 6, 2025

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

1.4K
Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.6K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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

Published on: May 17, 2019

9.0K

Area of Science:

  • Biomedical research
  • Bioinformatics
  • Cancer genomics

Background:

  • Pediatric cancer research presents unique challenges, including high-dimensional data and low sample sizes, due to the rarity of diseases.
  • Abundant resources are available for studying rare pediatric cancers, necessitating advanced analytical approaches.

Purpose of the Study:

  • To present novel statistical methods for analyzing pediatric cancer multi-omics data.
  • To provide tools for identifying cancer-driving genes and understanding molecular mechanisms.
  • To develop methods for predicting clinical outcomes and informing biological hypotheses.

Main Methods:

  • Genomic Random Interval (GRIN) method: Evaluates genomic abnormalities in tumor DNA to identify potential cancer-driving genes.
  • Association of Lesions with Expression (ALEX) method: Assesses the impact of genomic abnormalities on RNA transcription to inform molecular mechanism hypotheses.
  • Projection Onto the Most Interesting Statistical Evidence (PROMISE) method: Identifies omic features associated with clinical outcomes (prognosis).

Main Results:

  • The developed methods (GRIN, ALEX, PROMISE) are statistically robust and powerful for multi-omics data analysis.
  • These methods have led to fundamental biological discoveries supporting ongoing clinical trials.
  • Application on a T-cell acute lymphoblastic leukemia (T-ALL) dataset demonstrates their utility.

Conclusions:

  • The presented statistical methods offer valuable tools for pediatric cancer multi-omics research.
  • These methods facilitate the identification of key genes, molecular mechanisms, and prognostic markers.
  • The study provides reproducible code and data for applying these methods to T-ALL research.