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Related Concept Videos

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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Related Experiment Video

Updated: Jun 29, 2026

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies
09:32

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

Published on: September 23, 2014

Tumor classification ranking from microarray data.

Rattikorn Hewett1, Phongphun Kijsanayothin

  • 1Department of Computer Science, Texas Tech University, Abilene, TX 79601, USA. Rattikorn.Hewett@cs.ttu.edu

BMC Genomics
|October 10, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces the Multi-Dimensional Ranker (MDR) for cancer gene expression analysis, achieving high accuracy in tumor classification. MDR simplifies feature selection and provides informative rankings for cancer subtypes.

Related Experiment Videos

Last Updated: Jun 29, 2026

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies
09:32

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

Published on: September 23, 2014

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene expression profiles from microarray data are crucial for cancer diagnostics and understanding cancer development.
  • Current molecular tumor classifications can be imperfect, necessitating methods for ranking classification accuracy.
  • Classification ranking for high-dimensional microarray data is challenging due to the vast number of genes.

Purpose of the Study:

  • To investigate a novel method for enhancing tumor classification informativeness using classification rankings.
  • To explore the utility of a classification ranking method that requires no additional analysis and maintains good accuracy.
  • To assess the effectiveness of the Multi-Dimensional Ranker (MDR) algorithm in cancer gene expression analysis.

Main Methods:

  • Analysis of microarray data from 11 cancer types and subtypes using the Multi-Dimensional Ranker (MDR) algorithm.
  • MDR is a boosting-based ranking algorithm designed for high-dimensional data.
  • Comparison of MDR with other learning algorithms, including Support Vector Machine (SVM), based on AUC and overall accuracy.

Main Results:

  • MDR significantly reduced the number of predictor genes to at most nine, a substantial decrease from over 12,000 genes.
  • MDR achieved the highest Area Under the ROC Curve (AUC) for prostate cancer, acute lymphoblastic leukemia (ALL) and four ALL subtypes.
  • MDR demonstrated highly competitive results, yielding the highest average AUC (91.01%) and average overall accuracy (90.01%) in cancer expression analysis.

Conclusions:

  • Classification rankings from MDR offer a simple yet effective method for informative tumor classifications from gene expression data.
  • MDR can serve as a direct feature selection mechanism to identify genes relevant to tumor classification.
  • The MDR approach shows potential applicability to a wide range of classification problems involving microarray data.