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对生物医学实体链接模型的全面评估

David Kartchner^1,2, Jennifer Deng², Shubham Lohiya²

¹Enveda Biosciences.

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing

|May 17, 2024

概括

这项研究评估了九个生物医学实体链接 (BioEL) 模型,发现当前的方法与基因/蛋白质链接和上下文集成存在困难. 一个统一的框架和模型被释放,以帮助未来的研究.

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.
计算生物学计算生物学

背景情况:

生物医学实体链接 (BioEL) 对于将文本提及与UMLS和MeSH等结构化知识库连接至关重要.
现有的生物EL模型有很大的差异,需要采用标准化的评估方法.

研究的目的:

通过使用统一的框架,全面评估九个最先进的生物EL模型.
确定当前BioEL方法在准确性,速度,可用性,概括性和适应性方面的优缺点.
为未来的研究提供可重复的评估框架和发布的模型.

主要方法:

开发并应用一个统一的框架来评估九个BioEL模型.
基于准确性,速度,易用性,概括性和适应新本体学/数据集的可比模型.
量化了预处理步骤的影响,包括缩写检测.

主要成果:

目前的BioEL模型表现出显著的性能差距,特别是在将基因和蛋白质联系起来.
模型通常很难有效地结合上下文信息,以获得准确的实体歧义.
在不同的评估标准和数据集中观察到性能变化.

结论:

对于改进的BioEL方法存在重大需求,特别是对于基因和蛋白质实体.
加强上下文利用对于提高生物EL准确性至关重要.
发布的统一框架和模型将成为BioEL研究社区的宝贵资源.

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