dvcrn Target Novelty Scorer
ID: 177
Description
Score the novelty of biological targets based on literature mining. By analyzing literature in academic databases such as PubMed and PubMed Central, assess the research popularity, uniqueness, and innovation potential of target molecules in the research field.
Features
- 🔬 Literature Retrieval: Automatically retrieve literature related to targets from PubMed and other databases
- 📊 Novelty Scoring: Calculate target novelty score based on multi-dimensional indicators (0-100)
- 📈 Trend Analysis: Analyze temporal trends in target research
- 🧬 Cross-validation: Verify current research status of targets by combining multiple databases
- 📝 Report Generation: Generate detailed novelty analysis reports
Scoring Criteria
- Research Heat (0-25 points): Number of related publications and citations in recent years
- Uniqueness (0-25 points): Distinction from known popular targets
- Research Depth (0-20 points): Progress of preclinical/clinical research
- Collaboration Network (0-15 points): Diversity of research institutions/teams
- Temporal Trend (0-15 points): Research growth trends in recent years
Usage
Basic Usage
cd /Users/z04030865/.openclaw/workspace/skills/target-novelty-scorer
python scripts/main.py --target "PD-L1"
Advanced Options
python scripts/main.py \
--target "BRCA1" \
--db pubmed \
--years 10 \
--output report.json \
--format json
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
--target |
string | required | Target molecule name or gene symbol |
--db |
string | pubmed | Data source (pubmed, pmc, all) |
--years |
int | 5 | Analysis year range |
--output |
string | stdout | Output file path |
--format |
string | text | Output format (text, json, csv) |
--verbose |
flag | false | Verbose output |
Output Format
JSON Output
{
"target": "PD-L1",
"novelty_score": 72.5,
"confidence": 0.85,
"breakdown": {
"research_heat": 18.5,
"uniqueness": 20.0,
"research_depth": 15.2,
"collaboration": 12.0,
"trend": 6.8
},
"metadata": {
"total_papers": 15234,
"recent_papers": 3421,
"clinical_trials": 89,
"analysis_date": "2026-02-06"
},
"interpretation": "This target has moderate novelty, with moderate research heat in recent years..."
}
Dependencies
- Python 3.9+
- requests
- pandas
- biopython (Entrez API)
- numpy
API Requirements
- NCBI API Key (for PubMed retrieval)
- Optional: Europe PMC API
Installation
pip install -r requirements.txt
License
MIT License - Part of OpenClaw Bioinformatics Skills Collection
Risk Assessment
| Risk Indicator | Assessment | Level |
|---|---|---|
| Code Execution | Python scripts with tools | High |
| Network Access | External API calls | High |
| File System Access | Read/write data | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Data handled securely | Medium |
Security Checklist
- No hardcoded credentials or API keys
- No unauthorized file system access (../)
- Output does not expose sensitive information
- Prompt injection protections in place
- API requests use HTTPS only
- Input validated against allowed patterns
- API timeout and retry mechanisms implemented
- Output directory restricted to workspace
- Script execution in sandboxed environment
- Error messages sanitized (no internal paths exposed)
- Dependencies audited
- No exposure of internal service architecture
Prerequisites
# Python dependencies
pip install -r requirements.txt
Evaluation Criteria
Success Metrics
- Successfully executes main functionality
- Output meets quality standards
- Handles edge cases gracefully
- Performance is acceptable
Test Cases
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- Performance: Large dataset → Acceptable processing time
Lifecycle Status
- Current Stage: Draft
- Next Review Date: 2026-03-06
- Known Issues: None
- Planned Improvements:
- Performance optimization
- Additional feature support