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Related Concept Videos

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...
Mouse Models of Cancer Study02:43

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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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.
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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...
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.
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Mouse Models of Cancer Study02:43

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A multi-classification deep neural network for cancer type identification from high-dimension, small-sample and

Yifu Zeng1,2, Yixiang Zhang3, Zikai Xiao1

  • 1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.

Scientific Reports
|February 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-classification generative adversarial network with feature bundling (MGAN-FB) to improve cancer diagnosis from gene microarray data, effectively addressing high-dimension, small-sample, and imbalanced data challenges.

Keywords:
Cancer diagnosisGene microarray dataHigh dimensionalLow-sample-sizeMulti-class imbalance

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Gene microarray data presents challenges like high-dimensionality, small sample sizes, and multi-class imbalance, hindering accurate cancer diagnosis.
  • Traditional feature selection and classification algorithms struggle with these complex data characteristics, leading to suboptimal performance.
  • Deep learning, particularly generative adversarial networks (GANs), shows promise in bioinformatics but is limited in multi-class applications and high-dimensional sparse data.

Purpose of the Study:

  • To propose a novel Multi-Classification Generative Adversarial Network combined with Feature Bundling (MGAN-FB) model.
  • To address the challenges of high-dimension, small-sample, and multi-class imbalance in gene microarray data classification.
  • To enhance feature extraction and classification performance at both feature and algorithmic levels.

Main Methods:

  • Developed a deep encoder structure for the generator, incorporating Feature Bundling (FB) and Squeeze and Excite (SE) mechanisms for adaptive feature extraction.
  • Implemented a multi-classifier with a softmax module in the discriminator.
  • Designed a novel objective function encompassing encode, reconstruction, discrimination, and multi-classification losses to extend the GAN framework to multi-class problems.

Main Results:

  • The proposed MGAN-FB model achieved effective dimensionality reduction without significant information loss.
  • Experiments on eight gene microarray datasets demonstrated superior classification performance compared to seven other methods.
  • The model showed advantages in terms of classification accuracy, running time, and statistical significance.

Conclusions:

  • The MGAN-FB model effectively handles the complexities of gene microarray data for cancer diagnosis.
  • The integration of feature bundling and SE mechanisms enhances adaptive feature extraction.
  • This approach represents a significant advancement in applying generative adversarial networks to multi-class bioinformatics problems.