majiayu000 PRS-Net Deep Learning Agent
The PRS-Net Deep Learning Agent implements interpretable geometric deep learning for polygenic risk score prediction. PRS-Net models non-linear gene-gene interactions and biological network relationships to enhance disease prediction accuracy and improve cross-ancestry portability compared to traditional linear PRS methods.
When to Use This Skill
- When linear PRS methods show limited performance.
- For modeling complex gene-gene interactions.
- To improve PRS portability across ancestries.
- When biological interpretability of PRS is needed.
- For integrating pathway and network information.
Core Capabilities
Non-Linear PRS: Capture gene-gene interactions via deep learning.
Network Integration: Incorporate protein-protein interaction networks.
Interpretability: Identify important pathways and gene modules.
Cross-Ancestry Transfer: Improved portability via learned biology.
Multi-Task Learning: Joint modeling of related traits.
Uncertainty Quantification: Provide prediction confidence.
PRS-Net Architecture
| Component | Function | Innovation |
|---|---|---|
| Input Layer | Gene-level summaries | Aggregated variant effects |
| Network Encoder | PPI graph convolution | Biological structure |
| Attention Layer | Gene importance | Interpretability |
| Predictor | Disease/trait prediction | Non-linear mapping |
| Explanation | Pathway enrichment | Biological insights |
Comparison to Traditional PRS
| Aspect | Linear PRS | PRS-Net |
|---|---|---|
| Gene Interactions | Not modeled | GNN captures |
| Network Biology | Ignored | Integrated |
| Interpretability | Limited (SNP weights) | Pathway-level |
| Cross-Ancestry | Often poor | Improved |
| Computational Cost | Low | Moderate |
| Training Data Needed | Low | Moderate |
Workflow
Input: Individual genotypes, PPI network, training phenotypes.
Gene Summarization: Aggregate SNPs to gene-level scores.
Network Encoding: Learn representations on PPI graph.
Prediction: Non-linear disease risk prediction.
Interpretation: Extract important genes and pathways.
Cross-Ancestry: Apply to diverse populations.
Output: Risk scores, uncertainty, biological explanations.
Example Usage
User: "Calculate PRS-Net scores for Type 2 Diabetes with pathway-level interpretation."
Agent Action:
python3 Skills/Precision_Medicine/PRS_Net_Deep_Learning_Agent/prs_net_predict.py \
--genotypes cohort_genotypes.vcf.gz \
--ppi_network string_ppi.graphml \
--trait type2_diabetes \
--model_weights prs_net_t2d_v1.pt \
--interpret_pathways true \
--ancestry_calibration multi \
--output prs_net_results/
Input Requirements
| Input | Format | Purpose |
|---|---|---|
| Genotypes | VCF/PLINK | SNP data |
| PPI Network | GraphML, edge list | Gene relationships |
| Gene Mapping | BED | SNP-to-gene |
| Training Labels | Phenotype file | Model training |
| GWAS Summary | Optional | Initialization |
Output Components
| Output | Description | Format |
|---|---|---|
| PRS-Net Score | Non-linear polygenic score | .csv |
| Risk Percentile | Population ranking | .csv |
| Gene Importance | Attention weights | .csv |
| Pathway Enrichment | Top pathways | .csv |
| Module Visualization | Network subgraphs | .png |
| Uncertainty | Prediction confidence | .json |
Network Biology Integration
| Network | Source | Genes | Edges |
|---|---|---|---|
| STRING PPI | String-db | 19,000 | 5.5M |
| BioGRID | BioGRID | 18,000 | 1.2M |
| Reactome | Reactome | 10,000 | 250K |
| GO Biological Process | Gene Ontology | 18,000 | Hierarchical |
Performance Benchmarks
| Disease | Linear PRS AUC | PRS-Net AUC | Improvement |
|---|---|---|---|
| Type 2 Diabetes | 0.65 | 0.72 | +7% |
| Coronary Artery Disease | 0.70 | 0.76 | +6% |
| Schizophrenia | 0.62 | 0.68 | +6% |
| Alzheimer's Disease | 0.68 | 0.74 | +6% |
Cross-Ancestry Portability
| Ancestry | Linear PRS Drop | PRS-Net Drop |
|---|---|---|
| EUR → EAS | -15% | -8% |
| EUR → AFR | -30% | -18% |
| EUR → SAS | -20% | -12% |
| EUR → AMR | -18% | -10% |
AI/ML Components
Graph Neural Networks:
- Graph convolutional networks (GCN)
- Graph attention networks (GAT)
- Message passing neural networks
Interpretability:
- Attention visualization
- Integrated gradients
- Pathway enrichment analysis
Transfer Learning:
- Pre-training on EUR
- Fine-tuning on diverse
- Domain adaptation
Prerequisites
- Python 3.10+
- PyTorch, PyTorch Geometric
- NetworkX, igraph
- Scanpy (optional for visualization)
- GPU recommended
Related Skills
- Multi_Ancestry_PRS_Agent - Traditional multi-ancestry PRS
- PopEVE_Variant_Predictor_Agent - Variant interpretation
- Pharmacogenomics_Agent - Drug-gene interactions
- Pathway_Analysis - Pathway enrichment
Biological Interpretation
| Interpretation Level | Output | Clinical Use |
|---|---|---|
| Gene | Top contributing genes | Target identification |
| Pathway | Enriched pathways | Mechanism understanding |
| Module | Network subgraphs | Biological insight |
| Hub Genes | Central genes | Druggable targets |
Training Considerations
| Factor | Recommendation | Rationale |
|---|---|---|
| Sample Size | >10,000 | Deep learning needs data |
| Class Balance | Oversample or weight | Avoid bias |
| Validation | Cross-validation | Avoid overfitting |
| Regularization | Dropout, L2 | Generalization |
Special Considerations
- Interpretability Trade-offs: More complex = less interpretable
- Computational Requirements: GPU accelerates training
- Network Quality: PPI accuracy affects results
- Gene Mapping: SNP-to-gene assignment matters
- Overfitting: Regularization essential
Clinical Applications
| Application | PRS-Net Advantage | Benefit |
|---|---|---|
| Risk Stratification | Higher accuracy | Better prediction |
| Biological Insight | Pathway interpretation | Mechanism |
| Drug Targets | Hub gene identification | Therapeutic targets |
| Ancestry Equity | Better portability | Fairer prediction |
Limitations
| Limitation | Impact | Future Direction |
|---|---|---|
| Training Data | EUR-dominated | Diverse cohorts |
| Network Completeness | Missing edges | Multi-network integration |
| Rare Variants | Not well captured | WGS + rare variant methods |
| Clinical Validation | Limited trials | Prospective studies |
Author
AI Group - Biomedical AI Platform