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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...
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...
Epigenetic Regulation01:37

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
Epigenetic Regulation01:46

Epigenetic Regulation

Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
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: Jul 1, 2026

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
13:21

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

CancerSubtyper: a deep learning framework for cancer subtyping through DNA methylation data.

Joung Min Choi1, Yat Fei Cheung1, Liqing Zhang2,3

  • 1Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA.

Epigenetics & Chromatin
|June 30, 2026
PubMed
Summary
This summary is machine-generated.

CancerSubtyper offers a user-friendly web tool for deep learning-based cancer subtyping using DNA methylation data. This automated framework simplifies complex analyses, aiding precision oncology research.

Keywords:
DNA methylationDeep learningMolecular cancer subtyping

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Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

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Last Updated: Jul 1, 2026

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
13:21

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients

Published on: June 16, 2017

Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
07:50

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

Area of Science:

  • Computational Biology
  • Genomics
  • Oncology

Background:

  • Molecular subtyping is crucial for precision oncology, classifying tumors into relevant categories.
  • DNA methylation is a promising biomarker for cancer subtyping.
  • Current DNA methylation analysis faces challenges like high dimensionality, batch effects, and lack of user-friendly tools.

Purpose of the Study:

  • To present CancerSubtyper, an automated computational framework for deep learning-based cancer subtyping using DNA methylation data.
  • To provide an intuitive web interface for interactive exploration and downstream analysis of cancer subtypes.
  • To lower the barrier for large-scale methylation analysis in precision oncology research.

Main Methods:

  • Developed an end-to-end computational framework integrating semi-supervised and hybrid learning models.
  • Implemented automated preprocessing, feature selection, and batch correction.
  • Integrated interactive visualization tools for subtype exploration and validation.

Main Results:

  • CancerSubtyper provides a deep learning-based approach for cancer subtyping using DNA methylation.
  • The framework integrates two complementary models for classifying known subtypes and identifying novel ones.
  • An intuitive web interface facilitates interactive exploration and downstream analysis.

Conclusions:

  • CancerSubtyper offers an automated, end-to-end workflow for molecular subtyping via DNA methylation.
  • The user-friendly web interface makes large-scale methylation analysis more accessible.
  • This tool empowers precision oncology research by facilitating robust cancer subtyping.