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

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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

You might also read

Related Articles

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

Sort by
Same author

Inhibition of matrine against gastric cancer cell line MNK45 growth and its anti-tumor mechanism.

Molecular biology reports·2011
Same author

Evolution of activation patterns during long-duration ventricular fibrillation in pigs.

American journal of physiology. Heart and circulatory physiology·2011
Same author

A new feruloyl amide derivative from the fruits of Tribulus terrestris.

Natural product research·2011
Same author

High-amylose rice improves indices of animal health in normal and diabetic rats.

Plant biotechnology journal·2011
Same author

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

Statistical methods in medical research·2011
Same author

All-optical virtual private network and ONUs communication in optical OFDM-based PON system.

Optics express·2011

Related Experiment Videos

Gene network-based cancer prognosis analysis with sparse boosting.

Shuangge Ma1, Yuan Huang, Jian Huang

  • 1School of Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu

Genetics Research
|September 7, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces Network Sparse Boosting (NSBoost) for identifying cancer markers from gene expression data. NSBoost effectively selects key genes and modules, improving cancer prognosis prediction.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • High-throughput gene profiling is crucial for identifying cancer development and progression markers.
  • Weighted Gene Co-expression Network Analysis (WGCNA) reveals functional relationships between genes in modules.
  • Accurate identification of cancer markers requires accounting for gene network structures.

Purpose of the Study:

  • To develop a novel method for identifying cancer prognosis markers using gene expression data.
  • To integrate gene network module information into marker selection for improved accuracy.
  • To evaluate the proposed method against existing approaches in simulation and real-world data.

Main Methods:

  • Utilized Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene modules.
  • Developed a two-step sparse boosting approach, Network Sparse Boosting (NSBoost), for marker selection.
  • NSBoost performs within-module selection to create 'super markers', followed by module-level selection.

Main Results:

  • Simulation studies demonstrated that NSBoost more accurately identifies cancer-associated genes and modules than alternative methods.
  • Application to breast cancer and lymphoma prognosis data identified biologically significant genes.
  • NSBoost outperformed boosting and penalization approaches by selecting fewer genes/modules and achieving better prediction performance.

Conclusions:

  • Network Sparse Boosting (NSBoost) is an effective method for cancer marker discovery in prognosis studies.
  • Integrating gene network modules enhances the identification of relevant cancer markers.
  • NSBoost offers improved accuracy and prediction performance for cancer prognosis.