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HDGGCN: Heterogeneous Disease-Gene Network Representation Learning using Similarity-based Adjacency Matrix Generation
Summary
This study introduces HDGGCN, a novel method for predicting disease-related genes by integrating heterogeneous bioinformatics data. HDGGCN improves prediction accuracy, advancing precision medicine through better gene discovery.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Discovering disease-related genes is vital for understanding disease mechanisms and advancing precision medicine.
- Challenges in bioinformatics data, such as sparsity and complexity, hinder effective multi-source information fusion and feature extraction for accurate gene prediction.
- Existing methods struggle with high-dimensional sparse data, limiting prediction performance.
Purpose of the Study:
- To propose a novel heterogeneous disease-gene network representation method (HDGGCN) for improved disease-gene prediction.
- To address the limitations of sparse and complex bioinformatics data in gene discovery.
- To enhance clinical diagnosis and treatment through more accurate disease-gene associations.
Main Methods:
- Developed a cosine similarity-based adjacency matrix generation strategy to reconstruct a disease-gene-GO heterogeneous network.
- Employed a graph convolutional neural network (GCN) to generate low-dimensional node representations from reconstructed adjacency and feature matrices.
- Introduced a novel data partitioning mechanism to optimize prediction performance.
Main Results:
- The HDGGCN algorithm demonstrated significant effectiveness, validated by Precision, Recall, F1-score, and Association Precision (AP).
- Compared to baseline models, HDGGCN achieved performance improvements of 0.6%-2.4% in Precision, 1.0%-1.4% in Recall, and 1.0%-1.3% in F1-score.
- HDGGCN showed at least a 3.0% increase in Association Precision (AP), indicating superior predictive capability.
Conclusions:
- HDGGCN effectively overcomes challenges associated with high-dimensional sparse bioinformatics data for disease-gene prediction.
- The proposed method offers a robust approach for disease-gene association discovery, contributing to precision medicine.
- HDGGCN provides a significant advancement over existing methods, enhancing the accuracy of identifying disease-related genes.
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