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

Cancer Survival Analysis

Cancer Survival Analysis

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...

Adaptive Mechanisms in Cancer Cells

Adaptive Mechanisms in Cancer Cells

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,...

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

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...

You might also read

Related Articles

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

Sort by

Same author

Distinct senescent β-cell senotypes differentially drive islet aging and dysfunction.

bioRxiv : the preprint server for biology·2026

Same author

DIANNE: Segmentation-Free Localization of Histology Differential Attributes.

bioRxiv : the preprint server for biology·2026

Same author

Prediction of outcome from spatial Protein profiling of triple-negative breast cancers.

Communications medicine·2026

Same author

Interplay between cohesin and RNA polymerase II in regulating chromatin interactions and gene transcription.

Nature structural & molecular biology·2026

Same author

Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric Sarcoma Classification.

Cancer research·2026

Same author

SpliceDecoder: A High-Throughput Tool for Guiding the Functional Interpretation of Differential Splicing Events.

bioRxiv : the preprint server for biology·2025

Same journal

NanoporeDB: A Structural Resource Of Multimeric Protein Nanopores For Single-Molecule Sensing.

GigaScience·2026

Same journal

From the Brain Cell Atlas to Precision Neurology: A review of the application of AI-driven multi-omics in brain science.

GigaScience·2026

Same journal

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.

GigaScience·2026

Same journal

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026

Same journal

ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models.

GigaScience·2026

Same journal

ClusterGraph: a new tool for visualisation and compression of multidimensional data.

GigaScience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 6, 2026

Monitoring the Cancer-Immunity Cycle and Exploring Tumor Microenvironment Dynamics

Monitoring the Cancer-Immunity Cycle and Exploring Tumor Microenvironment Dynamics

Published on: June 7, 2024

Emerging AI approaches for cancer spatial omics.

Javad Noorbakhsh¹, Ali Foroughi Pour², Jeffrey Chuang^1,3

¹The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.

|October 16, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing spatial omics for cancer research. Developing interpretable spatial AI models is crucial for deciphering complex tumor biology from tissue data.

Keywords:

artificial intelligence deep learning foundation models spatial proteomics spatial transcriptomics tissue biophysics

More Related Videos

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Jan 6, 2026

Monitoring the Cancer-Immunity Cycle and Exploring Tumor Microenvironment Dynamics

Monitoring the Cancer-Immunity Cycle and Exploring Tumor Microenvironment Dynamics

Published on: June 7, 2024

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Spatial omics technologies generate high-resolution data on cellular and tissue architecture.
Artificial intelligence (AI) offers powerful tools for analyzing complex biological datasets.
Understanding the tumor microenvironment is critical for cancer diagnosis and treatment.

Purpose of the Study:

To review the current applications and future directions of AI in spatial omics for cancer research.
To highlight the challenges and opportunities in developing interpretable spatial AI models.
To discuss emerging paradigms for integrating AI with spatial omics data.

Main Methods:

Review of current literature on AI and spatial omics in cancer biology.
Discussion of state-of-the-art AI techniques applied to spatial tissue data.
Exploration of data-driven, constraint-based, and mechanistic spatial AI modeling approaches.

Main Results:

AI is essential for deciphering cancer biology from large-scale spatial omics data.
Interpretable spatial AI models require improved data integration and novel conceptual frameworks.
Emerging AI paradigms include data-driven, constraint-based, and mechanistic modeling.

Conclusions:

Integrating AI with hypothesis-driven strategies and model systems is key to unlocking the value of cancer spatial information.
Further development of interpretable spatial AI is needed to fully leverage spatial omics data.
AI-driven spatial omics holds significant promise for transforming cancer research and clinical applications.