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

Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

1.1K
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
1.1K
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

61
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
61
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

279
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
279
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

570
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
570

You might also read

Related Articles

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

Sort by
Same author

Comparative Effectiveness of Glucagon-like Peptide-1 Receptor Agonists Versus Oral Agents for Insulin Discontinuation in Type 2 Diabetes : A Target Trial Emulation.

Annals of internal medicine·2026
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.

Statistics in medicine·2026
Same author

DNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis.

Biometrics·2026
Same author

Analysis of cross-platform health communication with a network approach.

Biometrics·2025
Same author

Robust sparse Bayesian regression for longitudinal gene-environment interactions.

Journal of the Royal Statistical Society. Series C, Applied statistics·2025

Related Experiment Video

Updated: Feb 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.

Jingmao Li1, Shuangge Ma1

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.

Statistics in Medicine
|February 5, 2026
PubMed
Summary

Integrating omics and pathological imaging data improves cancer prognosis modeling. This novel approach uses parametric modeling for omics and deep neural networks for imaging, enhancing variable selection and prediction accuracy.

Keywords:
cancer prognosisdata integrationdeep neural networkpenalized selection

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

1.1K

Related Experiment Videos

Last Updated: Feb 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

1.1K

Area of Science:

  • Oncology
  • Bioinformatics
  • Medical Imaging

Background:

  • Prognostic modeling is crucial in cancer research.
  • Omics data and pathological imaging data offer complementary information for cancer prognosis.

Purpose of the Study:

  • To integrate omics and pathological imaging data within a Cox modeling framework.
  • To leverage the strengths of parametric and nonparametric approaches for distinct data types.

Main Methods:

  • Parametric modeling for omics variables (interpretability).
  • Nonparametric modeling using deep neural networks (DNNs) for pathological imaging features (flexibility and prediction).
  • Application of penalization for variable selection and estimation in high-dimensional data.

Main Results:

  • Demonstrated sensible variable selection.
  • Achieved superior prediction performance on TCGA data.
  • Highlighted the utility of integrating overlapping information from both data types.

Conclusions:

  • The proposed integrated approach offers practical utility for cancer prognosis.
  • Combining omics and imaging data enhances predictive accuracy.
  • The flexible modeling strategy accommodates distinct data properties effectively.