Target Novelty Scorer

Score the novelty of biological targets through literature mining and trend analysis

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

1. Research Heat (0-25 points): Number of related publications and citations in recent years
2. Uniqueness (0-25 points): Distinction from known popular targets
3. Research Depth (0-20 points): Progress of preclinical/clinical research
4. Collaboration Network (0-15 points): Diversity of research institutions/teams
5. 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

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. 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