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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.9K
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...
4.9K
Cancer Stem Cells and Tumor Maintenance02:40

Cancer Stem Cells and Tumor Maintenance

4.9K
Early diagnosis and treatment can often cure cancer. However, even with treatment, residual cells called cancer stem cells (CSC) might remain, often causing tumor recurrence. These cancer stem cells possess the potential for self-renewal and multi-lineage differentiation and are often responsible for the therapeutic resistance displayed in most cancers.
Cancer stem cells are thought to originate from tissue-specific normal stem cells or progenitor cells. The normal stem cells usually reside in...
4.9K
Cancer02:18

Cancer

48.1K
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.
48.1K
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

7.5K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
7.5K
Cancer Survival Analysis01:21

Cancer Survival Analysis

328
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...
328
Tumor Progression02:07

Tumor Progression

6.2K
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...
6.2K

You might also read

Related Articles

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

Sort by
Same author

Beyond Curated Knowledge: Structural Protein Embeddings Enhance GNN-Based Personalized Cancer Prognosis.

IEEE journal of biomedical and health informatics·2026
Same author

An exploratory study on predicting depressive symptoms in autistic individuals using wearable devices and machine learning.

Journal of the Formosan Medical Association = Taiwan yi zhi·2025
Same author

Improving Generalization in Collision Avoidance for Multiple Unmanned Aerial Vehicles via Causal Representation Learning.

Sensors (Basel, Switzerland)·2025
Same author

Urinary phthalate metabolites associate with blood levels of estrogen quinone-derived hemoglobin adducts in Taiwanese pregnant women.

Toxicology letters·2025
Same author

Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.

JACC. Asia·2025
Same author

Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: Jun 9, 2025

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

6.7K

Exploiting common patterns in diverse cancer types via multi-task learning.

Bo-Run Wu1, Sofia Ormazabal Arriagada2,3,4, Te-Cheng Hsu5

  • 1Graduate Institute of Communication Engineering, National Taiwan University (NTU), Taipei, Taiwan.

NPJ Precision Oncology
|October 30, 2024
PubMed
Summary
This summary is machine-generated.

Deep learning enhances cancer prognosis by integrating RNA Sequencing and clinical data. This multi-task approach improves predictions across diverse cancer types, aiding precision medicine.

More Related Videos

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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Related Experiment Videos

Last Updated: Jun 9, 2025

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

6.7K
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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

Area of Science:

  • Oncology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Conventional cancer prognosis methods face limitations with complex genetic biomarkers and diverse medical data.
  • Precision in identifying high-risk cancer patients is crucial for improving survival outcomes.

Purpose of the Study:

  • To develop a deep learning model for improved cancer prognosis prediction.
  • To explore shared patterns across different cancer types using multi-task learning.
  • To integrate RNA Sequencing and clinical data for enhanced predictive accuracy.

Main Methods:

  • Developed a multi-task bimodal neural network.
  • Integrated RNA Sequencing (RNA-Seq) and clinical data from The Cancer Genome Atlas (TCGA) datasets (Breast Invasive Carcinoma, Lung Adenocarcinoma, Colon Adenocarcinoma).
  • Performed external validation using Small Cell Lung Cancer data.

Main Results:

  • Significantly improved prognosis prediction, particularly for Colon Adenocarcinoma (up to 26% increase in concordance index and 41% in area under the precision-recall curve).
  • External validation demonstrated comparable metrics, suggesting benefits of supplementing small datasets.
  • The model effectively distils high-dimensional data into low-dimensional feature vectors.

Conclusions:

  • Multi-task learning shows promise for cancer prognosis prediction across diverse cancer types.
  • The approach can reveal shared underlying mechanisms among different cancers.
  • This study contributes to advancing precision medicine through improved prognostic capabilities.