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A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.

Zhenxiang Gao1, Yiheng Pan1, Pingjian Ding1

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AMIA ... Annual Symposium Proceedings. AMIA Symposium
|May 2, 2023
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Summary
This summary is machine-generated.

This study introduces GenePredict-KG, a novel system for predicting disease-gene associations using knowledge graphs. GenePredict-KG effectively integrates diverse biological data to enhance disease-gene prediction accuracy.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying disease-gene associations is crucial for understanding disease mechanisms, diagnostics, and therapeutics.
  • Existing computational methods often struggle to fully leverage heterogeneous topological and semantic information from multi-source biological data.
  • Predicting accurate disease-gene relationships remains a significant challenge in bioinformatics.

Purpose of the Study:

  • To propose GenePredict-KG, a knowledge graph-based system designed to improve disease-gene prediction.
  • To model and integrate semantic relations from various genotypic and phenotypic databases into a comprehensive knowledge graph.
  • To enhance the prediction of novel disease-gene interactions by utilizing learned representations from the knowledge graph.

Main Methods:

  • Construction of a large-scale knowledge graph with over 2.2 million associations between 73,000 entities, encompassing 14 relation types and 7 entity types.
  • Development of a knowledge graph embedding model to generate low-dimensional representations of entities and their relationships.
  • Application of these embeddings for inferring new disease-gene interactions and predicting potential associations.

Main Results:

  • GenePredict-KG demonstrated superior performance in disease-gene prediction compared to state-of-the-art methods.
  • Achieved high evaluation metrics, including an area under the receiver operating characteristic curve (AUROC) of 0.978, an area under the precision-recall curve (AUPR) of 0.343, and a mean reciprocal rank (MRR) of 0.244.
  • The system effectively leveraged topological and semantic information within the knowledge graph to enhance prediction accuracy.

Conclusions:

  • GenePredict-KG represents a significant advancement in computational methods for disease-gene association prediction.
  • The knowledge graph-based approach effectively integrates multi-source biological data, outperforming existing models.
  • This system holds promise for accelerating the discovery of diagnostic markers and therapeutic targets for various diseases.