Named Entity Recognition and Relationship Extraction in Biomedicine

从生物医学文献中挖掘有用的知识有助于文献检索，自动化生物数据管理和许多其他科学任务。因此，重要的是能够识别自由文本中的各种类型的生物实体，例如基因/蛋白质，疾病/病症和药物/化学品等。事实上，我们以前的PubMed日志分析显示，人们搜索某些生物医学概念的频率高于其他概念，并且不同概念之间存在很强的关联。例如，疾病名称通常与基因/蛋白质和药物名称共同出现。为了评估生物医学实体识别和关系提取的最新技术水平，我们在BioCreative V组织了一场科学竞赛，这是一项国际挑战活动，旨在评估生物学文本挖掘研究的进展。具体而言，我们设计了两个挑战性任务：疾病命名实体识别（DNER）和化学诱导疾病（CID）关系提取。为了帮助系统开发和评估，我们创建了一个大型注释文本语料库，其中包括来自1500篇PubMed文章的化学品，疾病及其相互作用的人类注释。全世界有34个团队参加了CDR任务：16个（DNER）和18个（CID）。最好的系统在DNER任务中获得了86.46%的F分数--接近人类注释者之间的一致性（0.8875）--而在CID任务中获得了57.03%的F分数，这是此类任务有史以来报告的最高结果。考虑到参与程度和团队成果，我们发现我们的任务成功地吸引了文本挖掘研究社区，产生了一个大型注释语料库，并改进了自动疾病识别和CDR提取的结果。 除了组织BioCreative任务外，我们还在2015-2016年继续开发生物医学命名实体标记器。首先，我们创建了一个名为TaggerOne的通用工具包：第一个用于联合命名实体识别和规范化的机器学习模型。TaggerOne是一个通用的标签器（即不特定于任何实体类型），只需要注释的训练数据和相应的词典，并且已经针对高吞吐量进行了优化。我们使用多个包含提及和概念级别注释的黄金标准语料库验证了TaggerOne。其结果与先前的最新技术相比是有利的，尽管该模型具有更大的灵活性。TaggerOne是用Java实现的，其源代码已向研究社区公开。 然而，大规模使用开源工具有时需要在基础设施和维护时间方面进行大量投资。这些投资不仅损害了文本挖掘工具的持续采用，而且还降低了单个研究人员探索将文本挖掘应用于其研究领域问题的能力。与此相反，Web服务使用简单的接口和数据格式通过Internet提供对软件工具的按需访问。因此，将文本挖掘工具作为Web服务提供，降低了生物学家和生物信息学研究人员使用的门槛，而不是专门从事文本挖掘，允许自由探索和专注于结果而不是方法的能力。 因此，在2015年，我们开发了NCBI文本挖掘Web服务，这是我们用于生物医学概念识别和信息提取的文本挖掘工具套件的在线版本。我们的服务结合了多种用于识别关键实体类型的最先进工具：DNorm（疾病），GNormPlus（基因和蛋白质），SR 4GN（物种），tmChem（化学品和药物）和tmVar（变体）。自成立以来，我们的网络服务已经处理了来自46个国家的研究人员的6000多万个请求，支持生物定位，众包和翻译生物信息学的研究项目。我们预计，提供Web服务的文本挖掘工具将大大扩大其效用的生物医学研究界。 最后，如前所述，文本挖掘研究的一个很有前途的应用领域是帮助手动文献管理，这是一个非常耗时和劳动密集型的过程。在这方面，我们继续改进我们以前的策展辅助工具PubTator，并与领域专家合作：在这种情况下是人类数据库策展者。通过这些努力，我们的PubTator系统每天都在两个外部数据库的生产策展管道中使用： 1. HuGE Navigator是CDC的人类基因组流行病学知识库 2. SwissProt：蛋白质序列和功能信息的注释数据库

Mining useful knowledge from the biomedical literature holds potentials for helping literature searching, automating biological data curation and many other scientific tasks. Hence, it is important to be able to recognize various types of biological entities in free text, such as gene/proteins, disease/conditions, and drug/chemicals, etc. Indeed, our previous PubMed log analysis revealed that people search certain biomedical concepts more often than others and that there exist strong associations between different concepts. For example, a disease name often co-occurs with gene/proteins and drug names. To assess the state of the art in biomedical entity recognition and relation extraction, we organized a science competition at BioCreative V, an international challenge event for evaluating advances in text mining research for biology. Specifically, we designed two challenge tasks: disease named entity recognition (DNER) and chemical-induced disease (CID) relation extraction. To assist system development and assessment, we created a large annotated text corpus that consisted of human annotations of chemicals, diseases and their interactions from 1500 PubMed articles. 34 teams worldwide participated in the CDR task: 16 (DNER) and 18 (CID). The best systems achieved an F-score of 86.46% for the DNER task--a result that approaches the human inter-annotator agreement (0.8875)--and an F-score of 57.03% for the CID task, the highest results ever reported for such tasks. Given the level of participation and team results, we found our task to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction. In addition to organizing the BioCreative task, we continued our own development of biomedical named entity taggers in 2015-2016. First and foremost, we created a general toolkit called TaggerOne: the first machine learning model for joint named entity recognition and normalization. TaggerOne is an all-purpose tagger (i.e. not specific to any entity type), requiring only annotated training data and a corresponding lexicon, and has been optimized for high throughput. We validated TaggerOne with multiple gold-standard corpora containing both mention- and concept-level annotations. Its results compare favorably to the previous state of the art, notwithstanding the greater flexibility of the model. TaggerOne is implemented in Java and its source code has been made publicly available to the research community. However, large-scale use of open-source tools sometimes requires a significant investment in infrastructure and maintenance time. These investments not only impair the continued adoption of text mining tools, but also reduce the ability of individual researchers to explore applying text mining to problems in their research area. In contrast, Web services provide on-demand access to software tools through the Internet using straightforward interfaces and data formats. Providing text mining tools as web services therefore reduces the bar to use for biocurators and bioinformatics researchers not working specifically in text mining, allowing free exploration and the ability to focus on results rather than methodology. Therefore, in 2015 we developed NCBI text-mining web services, an online version of our text mining tool suite for biomedical concept recognition and information extraction. Our service incorporates multiple state of the art tools for identifying critical entity types: DNorm (for diseases), GNormPlus (genes and proteins), SR4GN (species), tmChem (chemicals and drugs), and tmVar (variants). Our web service has already processed over 60 million requests since its inception from researchers in 46 countries, supporting research projects in biocuration, crowdsourcing and translational bioinformatics. We anticipate that providing text mining tools as web services will greatly expand their utility to the biomedical research community. Finally, as mentioned earlier, one promising application area for text mining research is to assist manual literature curation, a highly time-consuming and labor-intensive process. In this regard, we continued to improve our previous curation-assisting tool PubTator and to collaborate with domain experts: human database curators in this case. With these efforts, our PubTator system is continuously being used in the production curation pipeline of two external databases on a daily basis: 1. HuGE Navigator a CDCs knowledgebase of human genome epidemiology 2. SwissProt an annotated database of protein sequence and functional information