prs-net-deep-learning-agent

by FreedomIntelligence

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SKILL.md About

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---
name: 'prs-net-deep-learning-agent'
description: 'Geometric deep learning-based polygenic risk score prediction using PRS-Net for modeling gene interactions, enhanced disease prediction, and cross-ancestry portability.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---

# 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

1. **Non-Linear PRS**: Capture gene-gene interactions via deep learning.

2. **Network Integration**: Incorporate protein-protein interaction networks.

3. **Interpretability**: Identify important pathways and gene modules.

4. **Cross-Ancestry Transfer**: Improved portability via learned biology.

5. **Multi-Task Learning**: Joint modeling of related traits.

6. **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

1. **Input**: Individual genotypes, PPI network, training phenotypes.

2. **Gene Summarization**: Aggregate SNPs to gene-level scores.

3. **Network Encoding**: Learn representations on PPI graph.

4. **Prediction**: Non-linear disease risk prediction.

5. **Interpretation**: Extract important genes and pathways.

6. **Cross-Ancestry**: Apply to diverse populations.

7. **Output**: Risk scores, uncertainty, biological explanations.

## Example Usage

**User**: "Calculate PRS-Net scores for Type 2 Diabetes with pathway-level interpretation."

**Agent Action**:
```bash
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

1. **Interpretability Trade-offs**: More complex = less interpretable
2. **Computational Requirements**: GPU accelerates training
3. **Network Quality**: PPI accuracy affects results
4. **Gene Mapping**: SNP-to-gene assignment matters
5. **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