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

Manipulation and Analysis

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

You might also read

Related Articles

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

Sort by

Same author

MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis.

BMC genomics·2015

Same author

The I-TASSER Suite: protein structure and function prediction.

Nature methods·2014

Same author

Genome-wide expression analysis of soybean NF-Y genes reveals potential function in development and drought response.

Molecular genetics and genomics : MGG·2014

Same author

Classification of lung cancer using ensemble-based feature selection and machine learning methods.

Molecular bioSystems·2014

Same author

Resveratrol possesses protective effects in a pristane-induced lupus mouse model.

PloS one·2014

Same author

Protein-losing enteropathy in systemic lupus erythematosus: 12 years experience from a Chinese academic center.

PloS one·2014

Search research articles

Related Experiment Video

Updated: May 3, 2026

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

A contrastive learning approach to integrate spatial transcriptomics and histological images.

Yu Lin^1,2, Yanchun Liang^3,4, Duolin Wang²

¹School of Artificial Intelligence, Jilin University, Changchun 130012, China.

Computational and Structural Biotechnology Journal

|May 6, 2024

Summary

New models integrate gene expression, spatial location, and tissue morphology for improved spatial tissue architecture identification. These deep learning approaches offer better data representation for spatial transcriptomics analysis.

Keywords:

Contrastive learning Graph neural network Multi-modal data integration Spatial transcriptomics Tissue architecture identification

More Related Videos

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

Related Experiment Videos

Last Updated: May 3, 2026

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

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

Published on: May 3, 2024

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

Area of Science:

Genomics
Computational Biology
Biotechnology

Background:

Spatially resolved transcriptomics is rapidly advancing, offering new insights into tissue architecture.
Deep learning is used for spatial transcriptome analysis, but integrating multi-modal data remains a challenge.

Purpose of the Study:

To develop a novel model for integrating multi-modal spatial transcriptomics data.
To improve the accuracy of spatial tissue architecture identification using deep learning.

Main Methods:

Introduced ConGcR, a contrastive learning model integrating gene expression, spatial location, and tissue morphology.
Employed graph convolution and ResNet as encoders for different data modalities.
Enhanced ConGcR with a graph auto-encoder (ConGaR) for improved spatial representations.

Main Results:

Validated models on diverse human and animal tissues (brains, hearts, tumors, lungs).
Demonstrated superior performance in generating embeddings for tissue architecture identification compared to existing methods.
Achieved tissue architectures closer to ground truth.

Conclusions:

The developed models enhance the accuracy of spatial tissue architecture identification.
These models provide valuable data representation for various spatial transcriptomics analyses.
Offers improved insights into spatial tissue organization.