Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel Multilevel-graph neural network (GNN) to integrate multi-omics data and prior biological knowledge for improved disease survival risk prediction. The method enhances accuracy and identifies key molecular factors.
Area Of Science
- Bioinformatics
- Computational Biology
- Genomics
Background
- Multi-omics data integration offers insights into complex disease mechanisms but faces challenges like high dimensionality and limited samples.
- Prior biological knowledge, including gene regulatory networks and pathways, contains valuable gene interaction and functional information.
- Existing methods struggle to fully leverage the complementary nature of multi-omics data and prior knowledge.
Purpose Of The Study
- To develop a novel deep learning approach for effective multi-omics data integration.
- To incorporate prior biological knowledge (gene regulatory networks, pathways) into the analysis.
- To improve the accuracy of disease survival risk prediction using integrated data.
Main Methods
- Proposed a Multilevel-graph neural network (GNN), a hierarchical deep learning algorithm.
- Sequentially leveraged multi-omics data, gene regulatory networks, and pathway information for feature extraction.
- Employed GNN-Explainer and IGscore for interpreting neural network results at gene and pathway levels.
Main Results
- The proposed Multilevel-GNN achieved superior accuracy in survival risk prediction compared to existing methods.
- Identified key genes (e.g., SEC61G, CYP27B1) and pathways nonlinearly associated with tumor pathogenesis.
- Prioritized factors demonstrated strong associations with disease in survival analyses, with many previously reported in literature.
Conclusions
- The Multilevel-GNN effectively integrates multi-omics data and prior biological knowledge for enhanced disease survival risk prediction.
- The approach provides a powerful tool for uncovering complex molecular mechanisms underlying diseases.
- Interpretation algorithms successfully identified biologically relevant genes and pathways associated with disease prognosis.
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