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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...
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 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...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

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Related Experiment Video

Updated: Jun 13, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Cancer classification from gene expression data by NPPC ensemble.

Santanu Ghorai1, Anirban Mukherjee, Sanghamitra Sengupta

  • 1Department of Electronics and Communication Engineering, MCKV Institute of Engineering, 243, G.T. Road (N), Liluah, Howrah. san_ghorai@yahoo.co.in

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 19, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a nonparallel plane proximal classifier (NPPC) ensemble for accurate gene expression analysis in computer-aided diagnosis. The NPPC ensemble achieves high classification accuracy for tissue samples, comparable to SVM, with reduced training time.

Related Experiment Videos

Last Updated: Jun 13, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Medicine

Background:

  • Microarray technology is crucial for gene expression analysis and classifying tissue samples.
  • Accurate classification of tissue samples is vital for computer-aided diagnosis (CAD) systems.

Purpose of the Study:

  • To present a nonparallel plane proximal classifier (NPPC) ensemble for enhanced classification accuracy in CAD.
  • To introduce a novel minimum average proximity-based decision combiner for NPPC ensembles.
  • To compare the NPPC ensemble's performance against Support Vector Machine (SVM) classifiers.

Main Methods:

  • Gene selection using a mutual information criterion.
  • Genetic algorithm-based simultaneous feature and model selection for training NPPC experts.
  • Ensemble member selection based on validation set performance.
  • Implementation of a minimum average proximity-based decision combiner.

Main Results:

  • The NPPC ensemble demonstrated high classification accuracy for test samples.
  • The proposed ensemble achieved comparable testing accuracy to SVM ensembles.
  • The NPPC ensemble exhibited reduced average training times compared to SVM ensembles.

Conclusions:

  • The NPPC ensemble is effective for cancer diagnosis using gene expression data.
  • The minimum average proximity-based decision combiner enhances NPPC ensemble performance.
  • The NPPC ensemble offers a computationally efficient alternative to SVM for gene expression classification in CAD.