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

650
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...
650
Convolution Properties II01:17

Convolution Properties II

581
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
581
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.0K
Convolution Properties I01:20

Convolution Properties I

559
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
559
Ogive Graph01:07

Ogive Graph

6.6K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.6K

You might also read

Related Articles

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

Sort by
Same author

A Stretchable and Tough Graphene Film Enabled by Mechanical Bond.

Angewandte Chemie (International ed. in English)·2024
Same author

Corrigendum to "HSF1, in association with MORC2, downregulates ArgBP2 via the PRC2 family in gastric cancer cells" [Biochim. Biophys. Acta. Mol. Basis. Dis. 2018 Apr;1864(4 Pt A):1104-1114/PMID: 29339121].

Biochimica et biophysica acta. Molecular basis of disease·2024
Same author

Marine sulfated glycans inhibit the interaction of heparin with S-protein of SARS-CoV-2 Omicron XBB variant.

Glycoconjugate journal·2024
Same author

Formation of brominated and nitrated byproducts during unactivated peroxymonosulfate oxidation of phenol.

Journal of hazardous materials·2024
Same author

MAMLCDA: A Meta-Learning Model for Predicting circRNA-Disease Association Based on MAML Combined With CNN.

IEEE journal of biomedical and health informatics·2024
Same author

Editorial: Heparan sulfate-binding proteins in health and disease.

Frontiers in molecular biosciences·2024

Related Experiment Video

Updated: Jan 20, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

650

A Cancer Survival Prediction Method Based on Graph Convolutional Network.

Chunyu Wang, Junling Guo, Ning Zhao

    IEEE Transactions on Nanobioscience
    |August 24, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for cancer survival prediction by integrating multiple data types. The developed GCGCN model demonstrates superior accuracy compared to existing methods, improving patient outcome predictions.

    More Related Videos

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
    07:13

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

    Published on: April 18, 2025

    495
    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    69.7K

    Related Experiment Videos

    Last Updated: Jan 20, 2026

    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
    06:46

    Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

    Published on: September 27, 2024

    650
    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
    07:13

    Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

    Published on: April 18, 2025

    495
    A Protocol for Computer-Based Protein Structure and Function Prediction
    16:41

    A Protocol for Computer-Based Protein Structure and Function Prediction

    Published on: November 3, 2011

    69.7K

    Area of Science:

    • Oncology
    • Bioinformatics
    • Computational Biology

    Background:

    • Cancer poses a significant threat to human health due to its complexity and variable clinical outcomes.
    • Accurate cancer survival prediction is crucial but challenged by the limited use of single data types in current models.
    • Integration of diverse data sources is needed for comprehensive cancer analysis and improved prediction.

    Purpose of the Study:

    • To develop a novel method for cancer survival prediction by integrating multiple genomic and clinical data.
    • To enhance the accuracy of cancer patient survival time prediction through a comprehensive data-driven approach.

    Main Methods:

    • Proposed the Similar Network Fusion (SNF) algorithm to integrate multiple genomic data (gene expression, copy number alteration, DNA methylation, exon expression) and clinical data, generating a sample similarity matrix.
    • Utilized the min-redundancy and max-relevance (mRMR) algorithm for feature selection, creating a sample feature matrix.
    • Employed a graph convolutional network (GCN) for semi-supervised training using both matrices, resulting in the GCGCN model.

    Main Results:

    • The GCGCN model demonstrated that both multiple genomic data and clinical data are significant for accurate cancer survival time prediction.
    • Comparative analysis showed that the GCGCN model significantly outperforms existing survival prediction methods.
    • The integrated approach provides a superior prediction effect for cancer patient survival.

    Conclusions:

    • The GCGCN model effectively integrates multiple genomic and clinical data for cancer survival prediction.
    • The study validates the effectiveness and superiority of the GCGCN approach in predicting cancer survival.
    • This integrated method offers a promising advancement in personalized cancer care and treatment strategies.