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prs-net-deep-learning-agent
Maintainer FreedomIntelligence · Last updated April 1, 2026
Deep learning PRS prediction with PRSnet for complex traits.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/prs-net-deep-learning-agent
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- 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 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.
Source Doc
Excerpt From SKILL.md
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 |
Use cases
- 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.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream 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
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