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

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

5.7K
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,...
5.7K
Cancer Survival Analysis01:21

Cancer Survival Analysis

428
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...
428

You might also read

Related Articles

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

Sort by
Same author

scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.

Briefings in bioinformatics·2026
Same author

Beyond the canonical: The role of post-transcriptional regulation in drug-target interaction prediction.

PLoS computational biology·2026
Same author

Integrated Genomic and Epigenomic Analysis Reveals Epigenetic Plasticity in Disease Progression and Multidrug Resistance in Multiple Myeloma.

Cancer research·2026
Same author

EpGAT: integrating epigenetics and 3D genome structure to predict alternative splicing and polyadenylation.

Briefings in bioinformatics·2026
Same author

X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration.

Bioinformatics (Oxford, England)·2026
Same author

DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.

Bioinformatics advances·2026

Related Experiment Video

Updated: Aug 28, 2025

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
09:08

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

6.8K

omicsGAT: Graph Attention Network for Cancer Subtype Analyses.

Sudipto Baul1,2, Khandakar Tanvir Ahmed1,2, Joseph Filipek1,2

  • 1Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.

International Journal of Molecular Sciences
|September 23, 2022
PubMed
Summary

omicsGAT, a graph attention network, enhances cancer research by integrating gene expression data with attention mechanisms. This model effectively ranks neighbor importance for improved cancer outcome prediction and patient stratification.

Keywords:
cancer outcome predictiongraph attention networkpatient stratificationsingle-cell RNA-seq

More Related Videos

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

226
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

Related Experiment Videos

Last Updated: Aug 28, 2025

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
09:08

Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer

Published on: January 12, 2020

6.8K
Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

226
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K

Area of Science:

  • Biomedical Science
  • Genomics
  • Computational Biology

Background:

  • High-throughput omics technologies, including mRNA sequencing (RNA-seq), offer comprehensive molecular insights into cancer.
  • Graph-based learning models have been used to analyze gene expression data for cancer research but lack neighbor importance ranking.
  • Existing methods struggle to identify the varying influence of neighboring samples in cancer subtype analyses.

Purpose of the Study:

  • To introduce omicsGAT, a novel graph attention network (GAT) model for RNA-seq data analysis.
  • To integrate graph-based learning with an attention mechanism to improve cancer outcome prediction, patient stratification, and cell clustering.
  • To enable the ranking of neighbor importance for individual samples in cancer subtype analyses.

Main Methods:

  • Developed omicsGAT, a graph attention network (GAT) model utilizing a multi-head attention mechanism.
  • Applied omicsGAT to bulk and single-cell RNA-seq datasets from The Cancer Genome Atlas (TCGA) for breast and bladder cancers.
  • Evaluated the model's ability to integrate neighborhood information and generate an embedding vector for downstream tasks.

Main Results:

  • omicsGAT effectively integrates neighborhood information, learning embedding vectors that improve disease phenotype prediction, cancer patient stratification, and cell clustering.
  • The attention matrix generated by omicsGAT provides more valuable insights than traditional sample correlation-based adjacency matrices.
  • Demonstrated that specific neighbors hold greater significance than others in cancer subtype analyses, as indicated by attention coefficients.

Conclusions:

  • omicsGAT successfully leverages attention mechanisms to enhance the analysis of RNA-seq data in cancer research.
  • The model's ability to assign differential importance to neighbors represents a significant advancement in understanding cancer subtypes.
  • omicsGAT offers a powerful tool for uncovering complex relationships within gene expression data for improved biomedical applications.