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

291
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...
291
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.4K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.4K
Tumor Progression02:07

Tumor Progression

6.1K
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...
6.1K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.5K
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,...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Nonparametric change-point control charts for joint monitoring of mean and covariance with application to medical imaging data.

Statistical methods in medical research·2026
Same author

Research on polygonal microcavity 9 μm quantum cascade lasers with waveguide output from a master oscillator power amplifier.

Optics express·2026
Same author

Deciphering 6-mer Spectra Distribution Rules in Coronavirus Genomes: Application to Comparative Genomic Analysis.

International journal of molecular sciences·2026
Same author

Robust Self-Healing Omniphobic Coatings Enabled by Dynamic Networks of Polyhedral Oligomeric Silsesquioxane.

ACS applied materials & interfaces·2026
Same author

Research on Low Numerical Aperture 808 nm Fiber-Coupled Semiconductor Laser.

Micromachines·2026
Same author

Hormone Receptor Status as a Predictive Factor for Pathological Complete Response to Neoadjuvant Dual HER2 Blockade in Patients with HER2-Positive Breast Cancer: A Multicenter Retrospective Study.

Breast care (Basel, Switzerland)·2026

Related Experiment Video

Updated: May 9, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.4K

Cancer type and survival prediction based on transcriptomic feature map.

Ming Yan1, Zirou Dong1, Zhaopo Zhu2

  • 1Inner Mongolia Key Laboratory of Life Health and Bioinformatics, College of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou, 014010, China.

Computers in Biology and Medicine
|May 1, 2025
PubMed
Summary

This study developed a novel transcriptomic feature map using deep learning for cancer type and survival prediction. This approach achieved high accuracy, identifying key genes like ANXA5 and ACTB as potential cancer biomarkers.

Keywords:
Deep learningFeature mapPan-cancer analysisPrecision medicine

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599
Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.4K

Related Experiment Videos

Last Updated: May 9, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599
Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
07:47

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

1.4K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Cancer type and survival prediction remain challenging.
  • Transcriptomic data offers insights into cancer biology.
  • Current omics analysis methods can be improved for clinical application.

Purpose of the Study:

  • To develop a novel pan-cancer transcriptomic feature map for improved cancer type and survival prediction.
  • To identify potential cancer biomarkers using deep learning and network analysis.
  • To facilitate personalized cancer treatments through advanced omics analysis.

Main Methods:

  • Data cleaning, feature extraction, and visualization of TCGA transcriptomic and survival data.
  • Construction of a pan-cancer transcriptomic feature map.
  • Application of Inception networks and gated convolutional modules for classification.
  • Differential gene extraction and interaction network analysis.
  • Survival prediction using feature maps and data amplification.

Main Results:

  • A pan-cancer transcriptomic feature map was successfully constructed.
  • Pan-cancer classification accuracy reached 91.8% using deep learning models.
  • Two key genes, ANXA5 and ACTB, were identified as potential biomarkers.
  • Survival prediction accuracy ranged from 0.75 to 0.91 for 10 cancer types.

Conclusions:

  • The transcriptomic feature map offers a novel approach for cancer omics analysis.
  • Identified genes ANXA5 and ACTB show potential as biomarkers for cancer progression and treatment resistance.
  • This method can facilitate personalized cancer treatments by reflecting individual differences.