Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

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...

Protein Networks

Protein Networks

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,...

You might also read

Related Articles

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

Sort by

Same author

Integration of human and mouse single-cell transcriptomes of the developing cerebellum nominates cells-of-origin for group 3 and 4 medulloblastoma.

BMC cancer·2026

Same author

Epigenomic landscape of the developing human rhombic lip reveals gene regulatory network and non-coding loci of developmental, evolutionary, and disease relevance.

bioRxiv : the preprint server for biology·2025

Same author

Metformin reduces the competitive advantage of Dnmt3a<sup>R878H</sup> HSPCs.

Nature·2025

Same author

Metformin reduces the clonal fitness of <i>Dnmt3a</i> <sup>R878H</sup> hematopoietic stem and progenitor cells by reversing their aberrant metabolic and epigenetic state.

Research square·2024

Same author

Fourteen quick tips for crowdsourcing geographically linked data for public health advocacy.

PLoS computational biology·2023

Same author

Comparative Study of Clinical Severity and Biochemical Markers in Pre COVID-19 and COVID-19 Rhino-Orbito Cerebral Mucormycosis.

Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India·2023

Search research articles

Related Experiment Video

Updated: Oct 2, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Network Approaches for Precision Oncology.

¹Ontario Institute for Cancer Research, University of Toronto, Toronto, ON, Canada. shraddha.pai@utoronto.ca.

Advances in Experimental Medicine and Biology

|March 1, 2022

Summary

This summary is machine-generated.

Network-based computational methods are advancing precision oncology by integrating multi-omic tumor data. Patient similarity networks (PSNs) are key for stratifying and classifying patients, improving treatment design.

Keywords:

Classification Clustering Gene interaction networks Label propagation Machine learning Network Pathways Patient similarity network Supervised learning Unsupervised learning

More Related Videos

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

Related Experiment Videos

Last Updated: Oct 2, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

Published on: July 25, 2020

Area of Science:

Computational biology
Genomics
Oncology

Background:

Multi-omic tumor profiling and genomic regulatory networks offer new avenues for precision oncology.
Integrating heterogeneous data sources computationally is crucial for advancing personalized cancer treatments.
Genomic models benefit from interpretability and prior knowledge integration to minimize ungeneralizable models and optimize treatment strategies.

Purpose of the Study:

To introduce network-based approaches for integrating multi-modal data in precision oncology.
To highlight the application of patient similarity networks (PSNs) for patient stratification and classification.
To discuss methods for inferring driver mutations and future challenges in the field.

Main Methods:

Utilizing network-based approaches to integrate diverse data sources.
Employing patient similarity networks (PSNs) for patient stratification and classification.
Developing strategies for inferring driver mutations from individual patient mutation data.

Main Results:

Network-based methods enable the integration of multi-modal data for improved patient stratification.
Patient similarity networks (PSNs) show promise in classifying patients for tailored treatments.
Strategies for driver mutation inference are discussed in the context of sparse genetic data.

Conclusions:

Network-based approaches, particularly PSNs, are vital for advancing precision oncology.
Effective integration of multi-omic data and prior knowledge is essential for rational treatment design.
Overcoming current challenges will pave the way for future clinical applications in personalized cancer care.