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Related Concept Videos

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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 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...

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Related Experiment Video

Updated: Jun 25, 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

A Competitive Coevolution-based Cancer Driver Pathway Identification Algorithm for Maximizing Coverage, Mutual

HeJun Xu, Jingli Wu, Gaoshi Li

    IEEE Transactions on Computational Biology and Bioinformatics
    |June 23, 2026
    PubMed
    Summary
    This summary is machine-generated.

    A new computational model, Maximizing Coverage, Mutual exclusivity, and Subnet importance (MCMS), identifies cancer driver pathways by reducing bias from highly mutated genes. This approach enhances biological pathway enrichment and network connectivity for biomarker discovery.

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    Published on: October 3, 2025

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    Last Updated: Jun 25, 2026

    Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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    Published on: July 22, 2020

    Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
    03:08

    Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

    Published on: October 3, 2025

    Area of Science:

    • Computational biology
    • Genomics
    • Cancer research

    Background:

    • Identifying cancer driver pathways is essential for understanding cancer development.
    • Omics data allows computational inference of these pathways.
    • Existing methods may be biased towards highly mutated genes, potentially misidentifying drivers.

    Purpose of the Study:

    • To propose a novel parameter-free model, MCMS, for identifying cancer driver pathways.
    • To address the bias introduced by "dominant" genes in current identification methods.
    • To develop a competitive coevolution algorithm (CCA) to solve the MCMS model.

    Main Methods:

    • Developed MCMS model with a novel mutual exclusivity metric to mitigate "dominant" gene bias.
    • Devised a competitive coevolution algorithm (CCA-MCMS) with a fitness function for population diversity.
    • Evaluated gene sets against biological signaling pathways and the HINT-HI2012 network.

    Main Results:

    • CCA-MCMS identified gene sets with superior enrichment in biological signaling pathways.
    • Gene sets showed increased connectivity in the HINT-HI2012 network compared to other methods.
    • The method demonstrated significant computational efficiency.

    Conclusions:

    • CCA-MCMS effectively identifies cancer driver pathways with reduced bias.
    • The approach offers improved biological relevance and network integration.
    • The computational efficiency supports its application in biomarker discovery.