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