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

805
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...
805

You might also read

Related Articles

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

Sort by
Same author

Design, synthesis and performance evaluation of a fluorescent probe for the detection of sulfur dioxide <i>in vitro</i> and intracellularly.

Analytical methods : advancing methods and applications·2026
Same author

Molecular and clinical characterization of respiratory syncytial virus genotypes in a medical centre in Central Taiwan across the COVID-19 era.

Journal of infection and public health·2026
Same author

Case Report: Where is the boundary between autosomal recessive early-onset Parkinson's disease and dystonia-parkinsonism: a case of PLA2G6-associated neurodegeneration.

Frontiers in human neuroscience·2026
Same author

A hybrid radiomics framework integrating genetic algorithm-optimized random forest for preoperative identification of Luminal B breast cancer and Ki-67 prediction: A multicenter study.

The ultrasound journal·2026
Same author

Elucidating structure-function relationships in the mammalian nucleolus.

Nature reviews. Molecular cell biology·2026
Same author

DockingDB: An online reverse-docking database for plant hormone research.

Plant communications·2026

Related Experiment Video

Updated: Feb 28, 2026

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
09:08

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

7.3K

Identifying prognostic signature in ovarian cancer using DirGenerank.

Jian-Yong Wang1, Ling-Ling Chen1, Xiong-Hui Zhou1

  • 1College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China.

Oncotarget
|June 16, 2017
PubMed
Summary

This study identifies key prognostic genes for ovarian cancer using a novel algorithm, aiding in personalized treatment and drug discovery. The findings help distinguish patient risk and identify potential therapeutic targets.

Keywords:
DirGenerankbiomarkerdrug targetovarian cancerprognosis

More Related Videos

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

9.7K
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

8.0K

Related Experiment Videos

Last Updated: Feb 28, 2026

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
09:08

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

7.3K
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

9.7K
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
10:27

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

8.0K

Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Identifying prognostic genes is crucial for cancer treatment and drug discovery but challenging due to tumor heterogeneity.
  • Existing methods may select passenger genes rather than true prognostic modulators.
  • Ovarian cancer prognosis prediction requires robust methods to overcome data variability.

Purpose of the Study:

  • To develop and validate a novel algorithm, DirGenerank, for identifying prognostic gene signatures in ovarian cancer.
  • To distinguish cancer patient risk groups based on gene expression profiles.
  • To explore potential therapeutic targets among the identified prognostic genes.

Main Methods:

  • Constructed a gene dependency network using conditional mutual information from ovarian cancer gene expression and prognostic data.
  • Developed the DirGenerank algorithm to prioritize genes based on network centrality and prognostic correlation.
  • Utilized The Cancer Genome Atlas (TCGA) dataset for training and validated the prognostic signature on multiple independent datasets.

Main Results:

  • Identified a 40-gene prognostic signature with high predictive power for ovarian cancer patient outcomes.
  • The signature significantly distinguished prognostic risks across training, testing, and independent datasets.
  • Enrichment analysis suggested potential roles for signature genes as therapeutic drug targets.

Conclusions:

  • The proposed pipeline and DirGenerank algorithm effectively identify prognostic gene signatures for ovarian cancer.
  • The identified gene signature holds promise for improving patient risk stratification and guiding therapeutic strategies.
  • This approach offers a valuable tool for cancer prognosis and drug discovery efforts.