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

Combination Therapies and Personalized Medicine

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...
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

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...

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Related Experiment Videos

Cancer classification from the gene expression profiles by Discriminant Kernel-PLS.

Kai-Lin Tang1, Wei-Jia Yao, Tong-Hua Li

  • 1Shanghai Center for Bioinformation and Technology, Shanghai, P R China. kltang@scbit.org

Journal of Bioinformatics and Computational Biology
|December 15, 2010
PubMed
Summary

This study proposes using whole gene expression profiles for cancer classification, outperforming traditional gene selection methods. This approach offers improved accuracy for diagnosing diseases like leukemia and prostate cancer.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • Microarray technology is crucial for cancer diagnosis, providing gene expression profiles.
  • Current gene selection methods for cancer classification yield inconsistent results due to cancer's complexity.

Purpose of the Study:

  • To investigate cancer classification using whole gene expression profiles instead of selected gene sets.
  • To evaluate the NIPALS-KPLS method for analyzing gene expression data.

Main Methods:

  • Applied the NIPALS-KPLS method to analyze whole gene expression profiles.
  • Tested the method on three common cancer datasets: acute leukemia, prostate cancer, and lung cancer.

Main Results:

  • The NIPALS-KPLS method demonstrated significantly improved classification accuracy compared to conventional methods.
  • Analysis of whole gene expression profiles proved more effective than using partial gene sets.

Conclusions:

  • Whole gene expression profiles are a promising indicator for enhanced cancer classification.
  • The NIPALS-KPLS method shows potential for accurate and reliable cancer diagnosis.