Automatic bioinformatic software named entity recognition from literature

从文献中自动识别生物信息学软件命名实体

Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis.

摘要： 生物信息学软件和数据库是现代生命科学研究的重要组成部分，然而它们在科学文献中的提及方式往往不一致，且难以在大规模范围内进行系统性识别。缺乏全面且最新的生物信息学资源目录，阻碍了自动化生物医学知识提取和简化数据分析的进程。

Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations.

在此，我们提出了 SNAIL，这是一个混合命名实体识别框架，旨在从生物医学文本中自动识别生物信息学软件和数据库（SW/DB）名称。SNAIL 集成了互补的词汇和语义建模策略。词汇组件捕捉 SW/DB 名称特有的拼写模式和上下文线索，而语义组件则利用由 SciBERT 等基于 Transformer 的语言模型生成的上下文嵌入，并结合显式的标记掩码（token-masking）策略，以增强对实体的表征。

A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude.

通过一个结合了引文提示提取与大语言模型辅助蒸馏的混合流水线，我们自动构建了一个大型训练语料库。在两个独立基准数据集和真实研究论文上的评估表明，SNAIL 的表现显著优于现有方法，包括 bioNerDS2 等领域特定方法，以及 ChatGPT、Gemini、Grok 和 Claude 等通用大语言模型。

Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

将 SNAIL 应用于大规模文献分析，进一步揭示了不同生物信息学子领域在期刊层面的显著偏好差异。这些结果表明，SNAIL 为识别科学文本中的生物信息学资源提供了一种准确且可扩展的解决方案，并能够对工具使用情况和研究趋势进行系统性的元分析。