Enzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types. We developed an application of Snorkel, a weakly supervised learning framework, for extracting chemical reaction relationships from biomedical literature abstracts. For this work, we defined a chemical reaction relationship as the transformation of chemical A to chemical B. We built and evaluated our system on small annotated sets of chemical reaction relationships from two corpora: curated bacteria-related abstracts from the MetaCyc database (MetaCyc_Corpus) and a more general set of abstracts annotated with MeSH (Medical Subject Headings) term Bacteria (Bacteria_Corpus; a superset of MetaCyc_Corpus). For the MetaCyc_Corpus, we obtained 84% precision and 41% recall (55% F1 score). Extending to the more general Bacteria_Corpus decreased precision to 62% with only a four-point drop in recall to 37% (46% F1 score). Overall, the Bacteria_Corpus contained two orders of magnitude more candidate chemical reaction relationships (nine million candidates vs 68,0000 candidates) and had a larger class imbalance (2.5% positives vs 5% positives) as compared to the MetaCyc_Corpus. In total, we extracted 6871 chemical reaction relationships from nine million candidates in the Bacteria_Corpus. With this work, we built a database of chemical reaction relationships from almost 900,000 scientific abstracts without a large training set of labeled annotations. Further, we showed the generalizability of our initial application built on MetaCyc documents enriched with chemical reactions to a general set of articles related to bacteria.

中文翻译：

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使用 Snorkel 从文本中提取化学反应。

酶促和化学反应是理解细胞中生物过程的关键。存在化学反应的精选数据库，但这些数据库难以跟上生物医学文献的指数增长。传统的文本挖掘管道提供了从科学文献中自动提取实体和关系的工具，并部分取代了专家管理，但这种机器学习框架通常需要大量标记的训练数据，因此对于更大的文档语料库和新的关系类型都缺乏可扩展性. 我们开发了 Snorkel（一种弱监督学习框架）的应用程序，用于从生物医学文献摘要中提取化学反应关系。对于这项工作，我们将化学反应关系定义为化学物质 A 到化学物质 B 的转变。我们在来自两个语料库的少量带注释的化学反应关系集上构建和评估了我们的系统：来自 MetaCyc 数据库 (MetaCyc_Corpus) 的精选细菌相关摘要和一组用 MeSH（医学主题词）术语细菌 (Bacteria_Corpus; MetaCyc_Corpus 的超集）。对于 MetaCyc_Corpus，我们获得了 84% 的准确率和 41% 的召回率（55% F1 分数）。扩展到更一般的 Bacteria_Corpus 将精度降低到 62%，召回率仅下降了 4 个百分点，降至 37%（46% F1 分数）。总体而言，与 MetaCyc_Corpus 相比，Bacteria_Corpus 包含两个数量级以上的候选化学反应关系（900 万个候选对 68,0000 个候选），并且具有更大的类别不平衡（2.5% 阳性对 5% 阳性）。总共，我们从 Bacteria_Corpus 中的 900 万个候选对象中提取了 6871 个化学反应关系。通过这项工作，我们从近 900,000 篇科学摘要中建立了一个化学反应关系数据库，而无需大量训练有标签的注释。此外，我们展示了基于 MetaCyc 文档构建的初始应用程序的普遍性，该文档富含与细菌相关的一组通用文章的化学反应。