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

Cancer Survival Analysis01:21

Cancer Survival Analysis

455
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...
455
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

12.7K
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...
12.7K
Tumor Progression02:07

Tumor Progression

6.5K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
6.5K
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.1K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.1K

You might also read

Related Articles

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

Sort by
Same author

Integrated cross-organ transcriptomic analysis uncovers conserved gene signatures predictive of allograft rejection.

PloS one·2026
Same author

A single-cell network approach to decode metabolic regulation in gynecologic and breast cancers.

NPJ systems biology and applications·2025
Same author

IPD-Brain: An Indian histopathology dataset for glioma subtype classification.

Scientific data·2024
Same author

Gene expression signatures of stepwise progression of Hepatocellular Carcinoma.

PloS one·2023
Same author

Gastrointestinal manifestations in systemic lupus erythematosus: data from an Indian multi-institutional inception (INSPIRE) cohort.

Rheumatology (Oxford, England)·2023
Same author

AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images.

Cancers·2023

Related Experiment Video

Updated: Sep 11, 2025

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

19.5K

DeepGraphMut: a graph-based deep learning method for cancer prognosis using somatic mutation profile.

Aswin Jose1, Akansha Srivastava1, Ariba Ansari1

  • 1Centre for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Prof. C R Rao Road, Gachibowli, Hyderabad 500032, India.

Briefings in Bioinformatics
|August 15, 2025
PubMed
Summary

DeepGraphMut (DGM) identifies cancer subtypes using mutation data and protein networks. This graph-based deep learning tool aids in cancer prognosis and personalized medicine, even with limited data.

Keywords:
cancer subtype identificationgraph neural networkprotein–protein interaction networksomatic mutationsurvival prediction

More Related Videos

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Related Experiment Videos

Last Updated: Sep 11, 2025

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

19.5K
Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Area of Science:

  • Computational Biology
  • Genomics
  • Network Biology

Background:

  • Cancer's complexity and heterogeneity challenge subtype identification despite genomic advances.
  • Accurate cancer subtyping is crucial for effective treatment and prognosis.

Purpose of the Study:

  • To introduce DeepGraphMut (DGM), a novel graph-based deep-learning pipeline for cancer subtype identification.
  • To leverage somatic mutation data and protein-protein interaction (PPI) networks for patient-specific encodings.
  • To evaluate DGM's effectiveness in unsupervised and supervised cancer analyses.

Main Methods:

  • Developed DeepGraphMut (DGM), a graph-based deep-learning pipeline integrating somatic mutation data with PPI networks.
  • Employed a graph autoencoder with graph attention and a node-level attention decoder for generating patient-specific encodings.
  • Validated DGM on 7352 samples across 16 cancer types from The Cancer Genome Atlas (TCGA).

Main Results:

  • Unsupervised clustering identified distinct cancer subtypes with significant survival differences in 11 of 16 cancer types.
  • Supervised analysis using Cox regression demonstrated DGM's robust survival prediction performance (C-index ~0.7).
  • DGM outperformed its lightweight variant and other network-based methods in both unsupervised and supervised tasks.

Conclusions:

  • DeepGraphMut (DGM) provides a promising approach for cancer subtype discovery and prognosis, particularly in resource-limited settings.
  • The pipeline effectively utilizes somatic mutation data and network biology for personalized medicine applications.
  • DGM offers a valuable tool for advancing cancer research and clinical decision-making.