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Related Concept Videos

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 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.
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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.
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Related Experiment Video

Updated: Jun 5, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Improving cancer classification accuracy using gene pairs.

Pankaj Chopra1, Jinseung Lee, Jaewoo Kang

  • 1Department of Human Genetics, School of Medicine, Emory University, Atlanta, Georgia, United States of America.

Plos One
|January 5, 2011
PubMed
Summary
This summary is machine-generated.

Cancer biomarkers are improved by using gene pair combinations (doublets) instead of single genes. This approach enhances cancer classification accuracy across multiple datasets and algorithms, offering a more robust diagnostic strategy.

Related Experiment Videos

Last Updated: Jun 5, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Carcinogenesis may be triggered by pathway deregulation rather than individual genes.
  • Pathway deregulation often results from the simultaneous dysregulation of multiple genes within a pathway.
  • Gene pair combinations (doublets) may better capture underlying biomolecular reactions relevant to pathway deregulation.

Purpose of the Study:

  • To investigate if gene pair combinations (doublets) improve cancer classification accuracy compared to individual genes.
  • To validate the hypothesis that robust gene pair combinations serve as superior cancer biomarkers.

Main Methods:

  • Utilized gene pair combinations (doublets) as input for cancer classification algorithms.
  • Compared classification accuracy using doublets versus original gene expression values.
  • Validated the approach across nine cancer datasets and five classification algorithms: Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN).

Main Results:

  • Consistently improved classification accuracy across different datasets when using gene pair combinations.
  • Demonstrated the effectiveness of doublets as input for various classification algorithms.
  • The proposed approach showed enhanced performance in cancer classification tasks.

Conclusions:

  • Gene pair combinations (doublets) offer a more robust approach for cancer biomarker discovery and classification.
  • Utilizing doublets significantly enhances the accuracy of cancer classification algorithms.
  • This method provides a promising avenue for improving diagnostic strategies in oncology.