Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

15.2K
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...
15.2K
Cancer02:18

Cancer

55.2K
Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
55.2K
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

10.0K
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...
10.0K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

7.2K
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,...
7.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A scoping review of explainable artificial intelligence for medical multimodal data.

NPJ digital medicine·2026
Same author

Fairness in multimodal machine learning applications in clinical decision support: a systematic review.

NPJ digital medicine·2026
Same author

Prediction of incident atrial fibrillation from retinal fundus images using a multimodal foundation model.

NPJ digital medicine·2026
Same author

Capturing Finer-grained Long-range Dependency for Dense Prediction in Medical Images: An Empirical Investigation of MLPs.

IEEE journal of biomedical and health informatics·2026
Same author

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

IEEE journal of biomedical and health informatics·2026
Same author

Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.

Scientific data·2026

Related Experiment Video

Updated: Mar 8, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

20.0K

DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations.

Yuchen Yuan1,2, Yi Shi3, Changyang Li1

  • 1School of Information Technologies, The University of Sydney, Darlington, NSW, 2008, Australia.

BMC Bioinformatics
|February 4, 2017
PubMed
Summary

DeepGene, a novel deep neural network classifier, improves somatic point mutation-based cancer classification by addressing data sparsity and enhancing feature extraction. This method shows significant performance gains over existing classifiers.

More Related Videos

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

25.2K

Related Experiment Videos

Last Updated: Mar 8, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

20.0K
Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

25.2K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • DNA sequencing generates vast data for cancer research.
  • Somatic point mutations are key for cancer classification.
  • Existing methods face challenges with data sparsity and small sample sizes.

Purpose of the Study:

  • To develop an advanced classifier for somatic point mutation-based cancer classification (SMCC).
  • To overcome limitations of existing SMCC methods, including data sparsity and simple classifiers.

Main Methods:

  • Proposing DeepGene, a deep neural network (DNN) classifier.
  • Employing clustered gene filtering (CGF) to reduce irrelevant genes.
  • Utilizing indexed sparsity reduction (ISR) to mitigate data sparsity.
  • Integrating CGF and ISR outputs into a DNN for feature extraction.

Main Results:

  • DeepGene demonstrated improved classification performance on the TCGA-DeepGene dataset.
  • CGF, ISR, and DNN components each contributed to performance enhancement.
  • DeepGene achieved at least a 24% improvement in testing accuracy compared to existing classifiers.

Conclusions:

  • DeepGene effectively classifies cancer types using deep learning and somatic point mutation data.
  • The deep learning module in DeepGene excels at extracting high-level features.
  • DeepGene outperforms established classifiers, offering a more accurate SMCC approach.