从科学文献中对NLP衍生的化学基因/蛋白质关系进行大规模评估:对知识图构建的影响
Jonathan C G Jeynes1, Matthew Corney1, Tim James1
1Evotec (UK) Ltd., in silico Research and Development, Milton Park, Abingdon, Oxfordshire, United Kingdom.
PloS one
|September 8, 2023
概括
本研究评估了全球生物医学关系网络 (GNBR) 数据集,用于提取化学基因/蛋白质相互作用. 虽然表现适度,但高值识别了知识图构建的高保证关系.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 知识图 建筑 建筑知识图
背景情况:
- 活跃的研究重点是使用自然语言处理 (NLP) 来提取用于知识图 (KG) 构建的生物医学知识.
- 与实体共发生挖矿相比,从生物医学文本中准确地提取关系仍然是一个重大挑战.
研究的目的:
- 评估全球生物医学关系网络 (GNBR) 数据集,特别是其模拟化学物质和基因/蛋白质之间的关系的能力.
- 将GNBR的提取关系与化学蛋白相互作用的结构化数据库进行比较.
主要方法:
- 利用了GNBR数据集的一个子集,专注于化学基因/蛋白质关系.
- 使用Evotec的"Nexus"数据库 (>2.76M交互) 作为基准真相进行比较.
- 执行句对句的比较,以计算微平均精度回忆曲线下的面积 (AUC) 和接收器操作特征 (ROC) 曲线的AUC.
主要成果:
- 实现了微平均精度回忆AUC为0.50和微平均ROCAUC为0.71的关系类,包括"抑制"",绑定"",激进主义"和"对抗主义".
- 确定将高值应用于特定的关系类,如"抑制",可以产生结构化数据集中不存在的高可靠性三倍数.
结论:
- 评估表明,在关系提取中,GNBR数据集的表现得分不高.
- 特定关系类型的高值过显示了为KG构建生成高质量的数据的潜力.
- 提供一个合并的GNBR-Nexus (ChEMBL子集) 数据文件,以帮助药物发现策划工作.
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