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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,...

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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.

Surabhi Mishra¹, Gurjot Singh², Mahua Bhattacharya²

¹Department of Information Technology, ABV- Indian Institute of Information Technology and Management, Morena Road, Gwalior, 474015, Madhya Pradesh, India. surabhi@iiitm.ac.in.

Medical & Biological Engineering & Computing

|April 18, 2024

Summary

This summary is machine-generated.

This study introduces a novel heterogeneous network model to predict tissue-specific tumor-gene associations, crucial for personalized cancer therapies. The model achieved high accuracy, demonstrating its potential in advancing cancer research and treatment strategies.

Keywords:

Data integration Graph embedding Graph neural networks (GNNs)Heterogeneous network Tissue specific cancer research

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Tissue samples are vital for understanding tumor growth and patient health.
Establishing connections between tissue-specific tumor samples and genetic markers (genes) is key for personalized cancer therapies.

Purpose of the Study:

To construct a heterogeneous network integrating tumor sample-gene relations, gene-gene interactions, and tissue-specific gene expression data.
To leverage Graph Neural Networks (GNNs) for predicting tissue-specific tumor-gene associations.

Main Methods:

Development of a heterogeneous network model incorporating tumor sample-gene and gene-gene interaction data.
Inclusion of tissue-specific gene expression and primary site-based gene counts as network features.
Application of sampling-based GNNs and link layer embedding for link prediction.

Main Results:

The proposed model successfully predicted tumor-gene associations.
Achieved high performance metrics, with AUC-ROC scores reaching approximately 94%.
Demonstrated the efficacy of the heterogeneous network in predicting tissue-specific links.

Conclusions:

The heterogeneous network model shows significant potential for predicting tissue-specific tumor-gene links.
Findings underscore the importance of tissue-specific associations in advancing cancer research.
This approach supports the development of personalized cancer therapies.