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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 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...
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...
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...
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Mouse Models of Cancer Study

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Related Experiment Video

Updated: Jun 23, 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

Prediction of cancer class with majority voting genetic programming classifier using gene expression data.

Topon Kumar Paul1, Hitoshi Iba

  • 1System Engineering Laboratory, Corporate Research & Development Center, Toshiba Corporation, Kawasaki-shi, Kanagawa, Japan. toponkumar.paul@toshiba.co.jp

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 2, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Majority Voting Genetic Programming Classifier (MVGPC) to improve cancer classification from gene expression data. MVGPC enhances accuracy by using multiple evolved rules, outperforming existing methods and identifying potential cancer biomarkers.

Related Experiment Videos

Last Updated: Jun 23, 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:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression data analysis is crucial for understanding cancer and identifying biomarkers.
  • Machine learning methods often face overfitting challenges due to small sample sizes and imbalanced data in cancer genomics.

Purpose of the Study:

  • To develop an effective machine learning classifier for microarray data to improve cancer classification.
  • To address overfitting issues prevalent in current gene expression data analysis techniques.

Main Methods:

  • A novel Majority Voting Genetic Programming Classifier (MVGPC) was developed.
  • The MVGPC evolves multiple classification rules using genetic programming (GP).
  • A majority voting technique is applied to classify test samples based on evolved rules.

Main Results:

  • MVGPC demonstrated superior test accuracies compared to other methods, including AdaBoost with GP, across four diverse cancer datasets.
  • The classifier effectively handled multiclass cancer data.
  • Frequently occurring genes in the MVGPC classification rules are potential biomarkers for the studied cancers.

Conclusions:

  • The MVGPC is a robust and accurate method for cancer classification using gene expression data.
  • This approach mitigates overfitting issues common in high-dimensional genomic datasets.
  • The identified genes offer insights into cancer mechanisms and biomarker discovery.