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Related Concept Videos

Protein Networks02:26

Protein Networks

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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,...
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Related Experiment Video

Updated: Aug 28, 2025

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology
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GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry

Jin-Xian Hu1, Yang Yang2, Ying-Ying Xu3,4

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.

Bioinformatics (Oxford, England)
|September 16, 2022
PubMed
Summary

We developed GraphLoc, a deep graph convolutional neural network model, to accurately predict protein subcellular locations from images. This method aids in identifying cancer biomarkers and understanding protein functions.

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Protein subcellular localization is crucial for understanding protein function and disease.
  • Immunohistochemical (IHC) images are vital for visualizing protein distribution in tissues.
  • Existing image-based prediction methods struggle with multi-label proteins and pattern variations.

Purpose of the Study:

  • To develop an advanced model for accurate protein subcellular location prediction using IHC images.
  • To identify novel location biomarker proteins and protein network members in cancer tissues.

Main Methods:

  • Proposed GraphLoc, a multi-label, multi-instance deep graph convolutional neural network model.
  • Constructed graphs from multiple IHC images per protein for learning.
  • Employed graph convolutions for protein-level representation learning.
  • Utilized a dynamic threshold method for multi-label prediction.

Main Results:

  • GraphLoc demonstrated promising performance in image-based protein subcellular location prediction.
  • The model offers interpretability for its predictions.
  • Applied GraphLoc to identify candidate location biomarkers and protein network members.
  • A significant portion of predictions were supported by existing literature, with new candidates proposed.

Conclusions:

  • GraphLoc is an effective tool for protein subcellular location prediction from IHC images.
  • The model aids in biomarker discovery and understanding protein interaction networks.
  • Predicted candidates offer valuable directions for future experimental validation.