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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations.

Biffon Manyura Momanyi1, Yu-Wei Zhou2, Bakanina Kissanga Grace-Mercure3

  • 1School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.

Current Research in Structural Biology
|January 8, 2024
PubMed
Summary

Predicting small nucleolar RNA (snoRNA) and disease associations is vital for human health. A new Graph Neural Network model, SAGESDA, accurately identifies these links, improving diagnosis and treatment strategies.

Keywords:
DiseasesGraphSAGEHeterogeneous networkSmall nucleolar RNAssnoRNA-disease associations

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Small nucleolar RNAs (snoRNAs) play critical roles in biological processes linked to complex human diseases.
  • Accurate identification of snoRNA-disease associations is essential for disease diagnosis and treatment.
  • Traditional experimental methods for identifying these associations are costly and time-consuming, necessitating efficient computational approaches.

Purpose of the Study:

  • To develop a novel computational model for predicting snoRNA-disease associations.
  • To address the limitations of existing prediction models in terms of performance and efficiency.

Main Methods:

  • Introduction of SAGESDA, a Graph Neural Network (GNN) classification model utilizing the GraphSAGE architecture with attention.
  • Leveraging message passing in a heterogeneous network to generate node embeddings from neighboring nodes.
  • Employing mini-batch gradient descent for graph partitioning to enhance accuracy, speed, and scalability.

Main Results:

  • SAGESDA achieved a high predictive performance with an Area Under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.92.
  • The model demonstrated superior performance compared to previous related studies.
  • The approach proved effective in predicting unknown snoRNA-disease associations.

Conclusions:

  • SAGESDA is a highly accurate and efficient computational tool for predicting snoRNA-disease associations.
  • The model offers a promising solution for advancing disease diagnosis and treatment strategies.
  • The study highlights the potential of GNNs in uncovering complex biological relationships.