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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

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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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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 I: Proto-oncogenes01:33

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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: May 17, 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 module genes ranking using kernelized score functions.

Matteo Re1, Giorgio Valentini

  • 1Dipartimento di Informatica, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano MI, Italia.

BMC Bioinformatics
|October 26, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to identify cancer-driving genes by integrating various functional data. The approach effectively predicts cancer modules (CMs) and can discover novel genes involved in tumor development.

Related Experiment Videos

Last Updated: May 17, 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
  • Cancer Genomics

Background:

  • Cancer Modules (CMs) are gene sets crucial for cancer progression, often identified via gene expression.
  • Current methods relying solely on expression data may miss important cancer-related genes.
  • Integrating diverse functional interaction data is essential for a complete understanding of cancer mechanisms.

Purpose of the Study:

  • To develop a novel semi-supervised method for ranking genes within Cancer Modules (CMs).
  • To leverage integrated functional networks beyond gene expression data for improved gene discovery.
  • To enhance the prediction and identification of genes involved in cancer onset and progression.

Main Methods:

  • A semi-supervised learning approach using integrated functional networks.
  • Score functions incorporating local (guilt-by-association) and global (graph kernels) learning strategies.
  • Kernelized score functions designed for efficient processing of large gene networks.

Main Results:

  • The proposed method outperforms existing state-of-the-art gene ranking techniques.
  • Kernelized score functions demonstrate scalability for large-scale biological networks.
  • Successful prediction of CMs using integrated functional networks, complementing expression data.

Conclusions:

  • The modularity of kernelized score functions allows derivation of various gene ranking algorithms.
  • Integrated functional networks effectively predict CMs, improving upon expression-based methods.
  • The approach shows potential for discovering novel cancer-related genes and understanding tumor progression.