AA
AAV vector design: capsid selection, promoter optimization, payload capacity.
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数据与复现
PR
prs-net-deep-learning-agent
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Deep learning PRS prediction with PRSnet for complex traits.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/prs-net-deep-learning-agent
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- PRS-Net 深度学习 Agent implements interpretable geometric 深度学习 ，用于 polygenic risk score prediction. PRS-Net models non-linear gene-gene interactions 、 biological network relationships to enhance disease prediction accuracy 、 improve cross-ancestry portability compared to traditional linear PRS methods。
- When linear PRS methods show limited performance。
- 用于 modeling complex gene-gene interactions。
- To improve PRS portability across ancestries。
- When biological interpretability of PRS is needed。
原始文档
SKILL.md 摘录
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 |
适用场景
- When linear PRS methods show limited performance。
- 用于 modeling complex gene-gene interactions。
- To improve PRS portability across ancestries。
- When biological interpretability of PRS is needed。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- 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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