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

1. 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

CODEBLOCK0

Advanced Options

CODEBLOCK1

Parameters

Parameter	Type	Default	Description
INLINECODE0	string	required	Target molecule name or gene symbol
INLINECODE1

Output Format

JSON Output

CODEBLOCK2

Dependencies

- Python 3.9+
requests
pandas
biopython (Entrez API)
numpy

API Requirements

- NCBI API Key (for PubMed retrieval)
Optional: Europe PMC API

Installation

CODEBLOCK3

License

MIT License - Part of OpenClaw Bioinformatics Skills Collection

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts with tools	High
Network Access

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

CODEBLOCK4

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
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

靶标新颖性评分器

ID: 177

描述

基于文献挖掘对生物靶标的新颖性进行评分。通过分析PubMed和PubMed Central等学术数据库中的文献，评估靶标分子在研究领域中的研究热度、独特性和创新潜力。

功能特点

- 🔬 文献检索：自动从PubMed等数据库检索与靶标相关的文献
📊 新颖性评分：基于多维度指标计算靶标新颖性评分（0-100分）
📈 趋势分析：分析靶标研究的时间变化趋势
🧬 交叉验证：结合多个数据库验证靶标的当前研究状态
📝 报告生成：生成详细的新颖性分析报告

评分标准

1. 研究热度（0-25分）：近年相关发表物数量和引用次数
独特性（0-25分）：与已知热门靶标的区别程度
研究深度（0-20分）：临床前/临床研究进展
合作网络（0-15分）：研究机构/团队的多样性
时间趋势（0-15分）：近年研究增长趋势

使用方法

基本用法

bash
cd /Users/z04030865/.openclaw/workspace/skills/target-novelty-scorer
python scripts/main.py --target PD-L1

高级选项

bash
python scripts/main.py \
--target BRCA1 \
--db pubmed \
--years 10 \
--output report.json \
--format json

参数说明

参数	类型	默认值	描述
--target	字符串	必填	靶标分子名称或基因符号
--db

输出格式

JSON输出

json
{
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: 该靶标具有中等新颖性，近年研究热度适中...
}

依赖项

- Python 3.9+
requests
pandas
biopython（Entrez API）
numpy

API要求

- NCBI API密钥（用于PubMed检索）
可选：Europe PMC API

安装

bash
pip install -r requirements.txt

许可证

MIT许可证 - 属于OpenClaw生物信息学技能集的一部分

风险评估

风险指标	评估	等级
代码执行	带工具的Python脚本	高
网络访问

安全检查清单

- [ ] 无硬编码凭据或API密钥
[ ] 无未经授权的文件系统访问（../）
[ ] 输出不暴露敏感信息
[ ] 已实施提示注入防护
[ ] API请求仅使用HTTPS
[ ] 输入已根据允许模式进行验证
[ ] 已实现API超时和重试机制
[ ] 输出目录限制在工作空间内
[ ] 在沙盒环境中执行脚本
[ ] 错误消息已清理（不暴露内部路径）
[ ] 依赖项已审计
[ ] 不暴露内部服务架构

前置条件

bash

Python依赖项

pip install -r requirements.txt

评估标准

成功指标

- [ ] 成功执行主要功能
[ ] 输出符合质量标准
[ ] 优雅处理边缘情况
[ ] 性能可接受

测试用例

1. 基本功能：标准输入 → 预期输出
边缘情况：无效输入 → 优雅的错误处理
性能：大数据集 → 可接受的处理时间

生命周期状态

- 当前阶段：草稿
下次审查日期：2026-03-06
已知问题：无
计划改进：

- 性能优化 - 增加额外功能支持

target-novelty-scorer靶标新颖性评分